Pandemic associated changes in cognition from the CLSA Comprehensive Cohort

1) Data Processing

NOTE: This includes all the set-up and processing for the CLSA Comprehensive cohort data

1.1) Set-up

if (!require("pacman")) install.packages("pacman")

## Loading required package: pacman

pacman::p_load(MASS,plyr,ggplot2,lme4,nlme, rms,dplyr, lubridate, effects,
               lmerTest,rpart,tableone,psych,Hmisc,magrittr, ggeffects,sjmisc,splines,
               lsmeans,openxlsx)

set.seed(1)

setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data")

1.2) Tracking Cohort

1.2.1) Baseline processing

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#Baseline (Non-Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_BL")

trackBL<-read.csv("23SP001_McMaster_PRaina_Baseline_Trav4.csv")#Comprehensive Cohort Baseline

track.BL<-trackBL[c(1:5,104,926,798,106,892,921,32,1363,1377,1378,1379,1380,1407,1408,1433,1466,1432,1465,
                    1480,1481:1488,893,928,1093,1094,1096,1125,1124,376,1086,1089,1097:1102,1105,1106,
                    1129,1132,1133,1077,1076,1075,1078,1079,1085,1082,1130,1087,1090,1088,1131,1091,1080,
                    1128,1095,1081,334)]

#Sex
track.BL$Sex<-track.BL$SEX_ASK_TRM

#Age
track.BL$Age<-track.BL$AGE_NMBR_TRM

#Marital Status
track.BL$Relationship_status<-NA
track.BL$Relationship_status[track.BL$SDC_MRTL_TRM==1]<-"Single"
track.BL$Relationship_status[track.BL$SDC_MRTL_TRM==2]<-"Married"
track.BL$Relationship_status[track.BL$SDC_MRTL_TRM==3]<-"Widowed"
track.BL$Relationship_status[track.BL$SDC_MRTL_TRM==4]<-"Divorced"
track.BL$Relationship_status[track.BL$SDC_MRTL_TRM==5]<-"Separated"

#Education 4 Category
track.BL$Education4<-NA
track.BL$Education4[track.BL$ED_UDR04_TRM==1]<-"Less than High School Diploma"
track.BL$Education4[track.BL$ED_UDR04_TRM==2]<-"High School Diploma"
track.BL$Education4[track.BL$ED_UDR04_TRM==3]<-"Some College"
track.BL$Education4[track.BL$ED_UDR04_TRM==4]<-"College Degree or Higher"


#Household Income
track.BL$Income_Level<-NA
track.BL$Income_Level[track.BL$INC_PTOT_TRM==1]<-"<$20k"
track.BL$Income_Level[track.BL$INC_PTOT_TRM==2]<-"$20-50k"
track.BL$Income_Level[track.BL$INC_PTOT_TRM==3]<-"$50-100k"
track.BL$Income_Level[track.BL$INC_PTOT_TRM==4]<-"$100-150k"
track.BL$Income_Level[track.BL$INC_PTOT_TRM==5]<-">$150k"

#Living Status
track.BL$Living_status<-NA
track.BL$Living_status[track.BL$OWN_DWLG_TRM==1]<-"House"
track.BL$Living_status[track.BL$OWN_DWLG_TRM==2 |track.BL$OWN_DWLG_TRM==6]<-"Apartment/Condo/Townhome"
track.BL$Living_status[track.BL$OWN_DWLG_TRM==3]<-"Assisted Living"
track.BL$Living_status[track.BL$OWN_DWLG_TRM==4 | track.BL$OWN_DWLG_TRM==5 | track.BL$OWN_DWLG_TRM>=7]<-"Other"

#Alcohol      
track.BL$Alcohol<-NA
track.BL$Alcohol[track.BL$ALC_TTM_TRM==1]<-"Regular drinker (at least once a month)"
track.BL$Alcohol[track.BL$ALC_TTM_TRM==2]<-"Occasional drinker"
track.BL$Alcohol[track.BL$ALC_TTM_TRM==3]<-"Non-drinker"

#Smoking Status
track.BL$Smoking_Status<-NA
track.BL$Smoking_Status[track.BL$SMK_DSTY_TRM==1]<-"Daily Smoker"
track.BL$Smoking_Status[track.BL$SMK_DSTY_TRM==2 |track.BL$SMK_DSTY_TRM==3]<-"Occasional Smoker"
track.BL$Smoking_Status[track.BL$SMK_DSTY_TRM==4 | track.BL$SMK_DSTY_TRM==5]<-"Former Smoker"
track.BL$Smoking_Status[track.BL$SMK_DSTY_TRM==6]<-"Never Smoked"

track.BL$Ethnicity<-NA
track.BL$Ethnicity[track.BL$SDC_CULT_WH_TRM==1]<-"White"
track.BL$Ethnicity[track.BL$SDC_CULT_WH_TRM==0]<-"Other"

#############Physical Activity Scale for the Elderly######################
#Q1: Sitting Activity Frequency in Past 7 days
track.BL$PASE_Q1<-NA
track.BL$PASE_Q1[track.BL$PA2_SIT_MCQ==1]<-0
track.BL$PASE_Q1[track.BL$PA2_SIT_MCQ==2]<-0.11
track.BL$PASE_Q1[track.BL$PA2_SIT_MCQ==3]<-0.25
track.BL$PASE_Q1[track.BL$PA2_SIT_MCQ==4]<-0.43
track.BL$PASE_Q1[track.BL$PA2_SIT_MCQ>4]<-NA
track.BL$PASE_Q1[is.na(track.BL$PA2_SIT_MCQ)]<-NA

track.BL$PASE_Q1B<-NA
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ==1]<-0
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ==2]<-1
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ==3]<-3
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ==4]<-6
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ==5]<-10
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ>5]<- 0
track.BL$PASE_Q1B[is.na(track.BL$PA2_SITHR_MCQ)]<-NA
track.BL$PASE_Q1B<-as.numeric(track.BL$PASE_Q1B)

#Q2: Walking outside frequency
track.BL$PASE_Q2<- NULL
track.BL$PASE_Q2[track.BL$PA2_WALK_MCQ==1]<-0
track.BL$PASE_Q2[track.BL$PA2_WALK_MCQ==2]<-0.11
track.BL$PASE_Q2[track.BL$PA2_WALK_MCQ==3]<-0.25
track.BL$PASE_Q2[track.BL$PA2_WALK_MCQ==4]<-0.43
track.BL$PASE_Q2[track.BL$PA2_WALK_MCQ>4]<-NA
track.BL$PASE_Q2[is.na(track.BL$PA2_WALK_MCQ)]<-NA

track.BL$PASE_Q2A<- NA
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ==1]<-0
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ==2]<-1
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ==3]<-3
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ==4]<-6
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ==5]<-10
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ>5]<-NA
track.BL$PASE_Q2A[is.na(track.BL$PA2_WALKHR_MCQ)]<-NA
track.BL$PASE_Q2A<-as.numeric(track.BL$PASE_Q2A)


#Q3: Light Sports or Activity Frequency
track.BL$PASE_Q3<- NA
track.BL$PASE_Q3[track.BL$PA2_LSPRT_MCQ==1]<-0
track.BL$PASE_Q3[track.BL$PA2_LSPRT_MCQ==2]<-0.11
track.BL$PASE_Q3[track.BL$PA2_LSPRT_MCQ==3]<-0.25
track.BL$PASE_Q3[track.BL$PA2_LSPRT_MCQ==4]<-0.43
track.BL$PASE_Q3[track.BL$PA2_LSPRT_MCQ>4]<-NA
track.BL$PASE_Q3[is.na(track.BL$PA2_LSPRT_MCQ)]<-NA

track.BL$PASE_Q3A<- NA
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ==1]<-0
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ==2]<-1
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ==3]<-3
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ==4]<-6
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ==5]<-10
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ>5]<-NA
track.BL$PASE_Q3A[track.BL$PA2_LSPRT_MCQ == 1 | is.na(track.BL$PA2_LSPRTHR_MCQ)]<-0
track.BL$PASE_Q3A[is.na(track.BL$PA2_LSPRT_MCQ) & is.na(track.BL$PA2_LSPRTHR_MCQ)]<-NA
track.BL$PASE_Q3A[track.BL$PA2_LSPRT_MCQ>4 & is.na(track.BL$PA2_LSPRTHR_MCQ)]<-NA
track.BL$PASE_Q3A<-as.numeric(track.BL$PASE_Q3A)


#Q4: Moderate Sports or Activity Frequency
track.BL$PASE_Q4<-NA
track.BL$PASE_Q4[track.BL$PA2_MSPRT_MCQ==1]<-0
track.BL$PASE_Q4[track.BL$PA2_MSPRT_MCQ==2]<-0.11
track.BL$PASE_Q4[track.BL$PA2_MSPRT_MCQ==3]<-0.25
track.BL$PASE_Q4[track.BL$PA2_MSPRT_MCQ==4]<-0.43
track.BL$PASE_Q4[track.BL$PA2_MSPRT_MCQ>4]<-NA
track.BL$PASE_Q4[is.na(track.BL$PA2_MSPRT_MCQ)]<-NA

track.BL$PASE_Q4A<- NA
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ==1]<-0
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ==2]<-1
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ==3]<-3
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ==4]<-6
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ==5]<-10
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ>5]<-NA
track.BL$PASE_Q4A[track.BL$PA2_MSPRT_MCQ == 1 | is.na(track.BL$PA2_MSPRTHR_MCQ)]<-0
track.BL$PASE_Q4A[is.na(track.BL$PA2_MSPRT_MCQ) & is.na(track.BL$PA2_MSPRTHR_MCQ)]<-NA
track.BL$PASE_Q4A[track.BL$PA2_MSPRT_MCQ>4 & is.na(track.BL$PA2_MSPRTHR_MCQ)]<-NA
track.BL$PASE_Q4A<-as.numeric(track.BL$PASE_Q4A)


#Q5: Strenuous Sports or Activity Frequency
track.BL$PASE_Q5<-NA
track.BL$PASE_Q5[track.BL$PA2_SSPRT_MCQ==1]<-0
track.BL$PASE_Q5[track.BL$PA2_SSPRT_MCQ==2]<-0.11
track.BL$PASE_Q5[track.BL$PA2_SSPRT_MCQ==3]<-0.25
track.BL$PASE_Q5[track.BL$PA2_SSPRT_MCQ==4]<-0.43
track.BL$PASE_Q5[track.BL$PA2_SSPRT_MCQ>4]<-NA
track.BL$PASE_Q5[is.na(track.BL$PA2_SSPRT_MCQ)]<-NA

track.BL$PASE_Q5A<- NA
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ==1]<-0
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ==2]<-1
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ==3]<-3
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ==4]<-6
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ==5]<-10
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ>5]<-NA
track.BL$PASE_Q5A[track.BL$PA2_SSPRT_MCQ == 1 | is.na(track.BL$PA2_SSPRTHR_MCQ)]<-0
track.BL$PASE_Q5A[is.na(track.BL$PA2_SSPRT_MCQ) & is.na(track.BL$PA2_SSPRTHR_MCQ)]<-NA
track.BL$PASE_Q5A[track.BL$PA2_SSPRT_MCQ>4 & is.na(track.BL$PA2_SSPRTHR_MCQ)]<-NA
track.BL$PASE_Q5A<-as.numeric(track.BL$PASE_Q5A)


#Q6: Muscle strengthening and endurance exercise
track.BL$PASE_Q6<-NA
track.BL$PASE_Q6[track.BL$PA2_EXER_MCQ==1]<-0
track.BL$PASE_Q6[track.BL$PA2_EXER_MCQ==2]<-0.11
track.BL$PASE_Q6[track.BL$PA2_EXER_MCQ==3]<-0.25
track.BL$PASE_Q6[track.BL$PA2_EXER_MCQ==4]<-0.43
track.BL$PASE_Q6[track.BL$PA2_EXER_MCQ>4]<-NA
track.BL$PASE_Q6[is.na(track.BL$PA2_EXER_MCQ)]<-NA

track.BL$PASE_Q6A<- NA
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ==1]<-0
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ==2]<-1
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ==3]<-3
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ==4]<-6
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ==5]<-10
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ>5]<-NA
track.BL$PASE_Q6A[track.BL$PA2_EXER_MCQ == 1 | is.na(track.BL$PA2_EXERHR_MCQ)]<-0
track.BL$PASE_Q6A[is.na(track.BL$PA2_EXER_MCQ) & is.na(track.BL$PA2_EXERHR_MCQ)]<-NA
track.BL$PASE_Q6A[track.BL$PA2_EXER_MCQ>4 & is.na(track.BL$PA2_EXERHR_MCQ)]<-NA
track.BL$PASE_Q6A<-as.numeric(track.BL$PASE_Q6A)


#Q7: Light Housework
track.BL$PASE_Q7<-NA
track.BL$PASE_Q7[track.BL$PA2_LTHSWK_MCQ==1]<-1
track.BL$PASE_Q7[track.BL$PA2_LTHSWK_MCQ==2]<-0
track.BL$PASE_Q7[track.BL$PA2_LTHSWK_MCQ>2]<-NA
track.BL$PASE_Q7[is.na(track.BL$PA2_LTHSWK_MCQ)]<-NA
track.BL$PASE_Q7<-as.numeric(track.BL$PASE_Q7)


#Q8: Heavy Housework
track.BL$PASE_Q8<-NA
track.BL$PASE_Q8[track.BL$PA2_HVYHSWK_MCQ==1]<-1
track.BL$PASE_Q8[track.BL$PA2_HVYHSWK_MCQ==2]<-0
track.BL$PASE_Q8[track.BL$PA2_HVYHSWK_MCQ>2]<-NA
track.BL$PASE_Q8[is.na(track.BL$PA2_HVYHSWK_MCQ)]<-NA
track.BL$PASE_Q8<-as.numeric(track.BL$PASE_Q8)


#Q9: Home Repair, Yardwork, Gardening, Care for another person
track.BL$PASE_Q9A<-NA
track.BL$PASE_Q9A[track.BL$PA2_HMREPAIR_MCQ==1]<-1
track.BL$PASE_Q9A[track.BL$PA2_HMREPAIR_MCQ==2]<-0
track.BL$PASE_Q9A[track.BL$PA2_HMREPAIR_MCQ>2]<-NA
track.BL$PASE_Q9A[is.na(track.BL$PA2_HMREPAIR_MCQ)]<-NA
track.BL$PASE_Q9A<-as.numeric(track.BL$PASE_Q9A)

track.BL$PASE_Q9B<-NA
track.BL$PASE_Q9B[track.BL$PA2_HVYODA_MCQ==1]<-1
track.BL$PASE_Q9B[track.BL$PA2_HVYODA_MCQ==2]<-0
track.BL$PASE_Q9B[track.BL$PA2_HVYODA_MCQ>2]<-NA
track.BL$PASE_Q9B[is.na(track.BL$PA2_HVYODA_MCQ)]<-NA
track.BL$PASE_Q9B<-as.numeric(track.BL$PASE_Q9B)

track.BL$PASE_Q9C<-NA
track.BL$PASE_Q9C[track.BL$PA2_LTODA_MCQ==1]<-1
track.BL$PASE_Q9C[track.BL$PA2_LTODA_MCQ==2]<-0
track.BL$PASE_Q9C[track.BL$PA2_LTODA_MCQ>2]<-NA
track.BL$PASE_Q9C[is.na(track.BL$PA2_LTODA_MCQ)]<-NA
track.BL$PASE_Q9C<-as.numeric(track.BL$PASE_Q9C)

track.BL$PASE_Q9D<-NA
track.BL$PASE_Q9D[track.BL$PA2_CRPRSN_MCQ==1]<-1
track.BL$PASE_Q9D[track.BL$PA2_CRPRSN_MCQ==2]<-0
track.BL$PASE_Q9D[track.BL$PA2_CRPRSN_MCQ>2]<-NA
track.BL$PASE_Q9D[is.na(track.BL$PA2_CRPRSN_MCQ)]<-NA
track.BL$PASE_Q9D<-as.numeric(track.BL$PASE_Q9D)

#Q10: Working and Volunteering
track.BL$PASE_Q10<-NA
track.BL$PASE_Q10[track.BL$PA2_WRK_MCQ==1]<-1
track.BL$PASE_Q10[track.BL$PA2_WRK_MCQ==2]<-0
track.BL$PASE_Q10[track.BL$PA2_WRK_MCQ>2]<-NA
track.BL$PASE_Q10[is.na(track.BL$PA2_WRK_MCQ)]<-NA
track.BL$PASE_Q10<-as.numeric(track.BL$PASE_Q10)

track.BL$PASE_Q10A<-NA
track.BL$PASE_Q10A<-track.BL$PA2_WRKHRS_NB_MCQ
track.BL$PASE_Q10A[track.BL$PA2_WRKHRS_NB_MCQ>=700]<-NA
track.BL$PASE_Q10A[track.BL$PA2_WRK_MCQ == 2 | is.na(track.BL$PA2_WRKHRS_NB_MCQ)]<-0
track.BL$PASE_Q10A[is.na(track.BL$PA2_WRK_MCQ) & is.na(track.BL$PA2_WRKHRS_NB_MCQ)]<-NA
track.BL$PASE_Q10A<-as.numeric(track.BL$PASE_Q10A)
track.BL$PASE_Q10A<-track.BL$PASE_Q10A/7

#PASE TOTAL SCORE#
track.BL$PASE_TOTAL<-track.BL$PASE_Q2*track.BL$PASE_Q2A*20 + track.BL$PASE_Q3*track.BL$PASE_Q3A*21 +  track.BL$PASE_Q4*track.BL$PASE_Q4A*23 + track.BL$PASE_Q5*track.BL$PASE_Q5A*30 +
  track.BL$PASE_Q6*track.BL$PASE_Q6A*30 + (track.BL$PASE_Q7+track.BL$PASE_Q8)*25 + track.BL$PASE_Q9A*30 + track.BL$PASE_Q9B*36 + track.BL$PASE_Q9C*20 + track.BL$PASE_Q9D*35 + 
  track.BL$PASE_Q10*track.BL$PASE_Q10A*21


#BMI
track.BL$BMI<-track.BL$HWT_DBMI_TRM
track.BL$BMI[track.BL$HWT_DBMI_TRM>100]<-NA

#CESD-10
track.BL$CESD_10<-track.BL$DEP_CESD10_TRM
track.BL$CESD_10[track.BL$DEP_CESD10_TRM>50]<-NA
track.BL$CESD_10[track.BL$DEP_CESD10_TRM==-88]<-NA

#Subjective Cognitive Impairment
track.BL$SCI<- NA
track.BL$SCI[track.BL$CCT_MEMPB_TRM==1]<- "Yes"
track.BL$SCI[track.BL$CCT_MEMPB_TRM==2]<- "No"

#Dementia and AD
track.BL$Dementia<- NA
track.BL$Dementia[track.BL$CCT_ALZH_TRM==1]<- "Yes"
track.BL$Dementia[track.BL$CCT_ALZH_TRM==2]<- "No"

#Anxiety
track.BL$Anxiety<- NA
track.BL$Anxiety[track.BL$CCT_ANXI_TRM==1]<- "Yes"
track.BL$Anxiety[track.BL$CCT_ANXI_TRM==2]<- "No"

#Mood Disorders
track.BL$Mood_Disord<- NA
track.BL$Mood_Disord[track.BL$CCT_MOOD_TRM==1]<- "Yes"
track.BL$Mood_Disord[track.BL$CCT_MOOD_TRM==2]<- "No"

#Pet Ownership at Baseline
track.BL$Pet_Owner<-NA
track.BL$Pet_Owner[track.BL$SSA_PET_TRM==1]<-"Yes"
track.BL$Pet_Owner[track.BL$SSA_PET_TRM==2]<-"No"

#Number of Chronic Conditions
track.BL$Chronic_conditions<-NA

track.BL$CCT_HEART_TRM[track.BL$CCT_HEART_TRM==1]<- 1 #Heart Disease
track.BL$CCT_HEART_TRM[track.BL$CCT_HEART_TRM==2]<- 0
track.BL$CCT_HEART_TRM[track.BL$CCT_HEART_TRM==8]<- NA
track.BL$CCT_HEART_TRM[track.BL$CCT_HEART_TRM==9]<- NA

track.BL$CCT_PVD_TRM[track.BL$CCT_PVD_TRM==1]<- 1 #peripheral vascular disease
track.BL$CCT_PVD_TRM[track.BL$CCT_PVD_TRM==2]<- 0
track.BL$CCT_PVD_TRM[track.BL$CCT_PVD_TRM==8]<- NA
track.BL$CCT_PVD_TRM[track.BL$CCT_PVD_TRM==9]<- NA

track.BL$CCT_MEMPB_TRM[track.BL$CCT_MEMPB_TRM==1]<- 1 #SCI
track.BL$CCT_MEMPB_TRM[track.BL$CCT_MEMPB_TRM==2]<- 0
track.BL$CCT_MEMPB_TRM[track.BL$CCT_MEMPB_TRM==8]<- NA
track.BL$CCT_MEMPB_TRM[track.BL$CCT_MEMPB_TRM==9]<- NA

track.BL$CCT_ALZH_TRM[track.BL$CCT_ALZH_TRM==1]<- 1 #Alzheimers or demeinta
track.BL$CCT_ALZH_TRM[track.BL$CCT_ALZH_TRM==2]<- 0
track.BL$CCT_ALZH_TRM[track.BL$CCT_ALZH_TRM==8]<- NA
track.BL$CCT_ALZH_TRM[track.BL$CCT_ALZH_TRM==9]<- NA

track.BL$CCT_MS_TRM[track.BL$CCT_MS_TRM==1]<- 1 #Multiple sclerosis
track.BL$CCT_MS_TRM[track.BL$CCT_MS_TRM==2]<- 0
track.BL$CCT_MS_TRM[track.BL$CCT_MS_TRM==8]<- NA
track.BL$CCT_MS_TRM[track.BL$CCT_MS_TRM==9]<- NA

track.BL$CCT_EPIL_TRM[track.BL$CCT_EPIL_TRM==1]<- 1 #Epilepsy
track.BL$CCT_EPIL_TRM[track.BL$CCT_EPIL_TRM==2]<- 0
track.BL$CCT_EPIL_TRM[track.BL$CCT_EPIL_TRM==8]<- NA
track.BL$CCT_EPIL_TRM[track.BL$CCT_EPIL_TRM==9]<- NA

track.BL$CCT_MGRN_TRM[track.BL$CCT_MGRN_TRM==1]<- 1 #Migraine headaches
track.BL$CCT_MGRN_TRM[track.BL$CCT_MGRN_TRM==2]<- 0
track.BL$CCT_MGRN_TRM[track.BL$CCT_MGRN_TRM==8]<- NA
track.BL$CCT_MGRN_TRM[track.BL$CCT_MGRN_TRM==9]<- NA

track.BL$CCT_ULCR_TRM[track.BL$CCT_ULCR_TRM==1]<- 1 #Intenstinal or stomach ulcers
track.BL$CCT_ULCR_TRM[track.BL$CCT_ULCR_TRM==2]<- 0
track.BL$CCT_ULCR_TRM[track.BL$CCT_ULCR_TRM==8]<- NA
track.BL$CCT_ULCR_TRM[track.BL$CCT_ULCR_TRM==9]<- NA

track.BL$CCT_IBDIBS_TRM[track.BL$CCT_IBDIBS_TRM==1]<- 1 #Bowel disorder
track.BL$CCT_IBDIBS_TRM[track.BL$CCT_IBDIBS_TRM==2]<- 0
track.BL$CCT_IBDIBS_TRM[track.BL$CCT_IBDIBS_TRM==8]<- NA
track.BL$CCT_IBDIBS_TRM[track.BL$CCT_IBDIBS_TRM==9]<- NA

track.BL$CCT_BOWINC_TRM[track.BL$CCT_BOWINC_TRM==1]<- 1 #Bowel incontinence
track.BL$CCT_BOWINC_TRM[track.BL$CCT_BOWINC_TRM==2]<- 0
track.BL$CCT_BOWINC_TRM[track.BL$CCT_BOWINC_TRM==8]<- NA
track.BL$CCT_BOWINC_TRM[track.BL$CCT_BOWINC_TRM==9]<- NA

track.BL$CCT_URIINC_TRM[track.BL$CCT_URIINC_TRM==1]<- 1 #Urinary incontinence
track.BL$CCT_URIINC_TRM[track.BL$CCT_URIINC_TRM==2]<- 0
track.BL$CCT_URIINC_TRM[track.BL$CCT_URIINC_TRM==8]<- NA
track.BL$CCT_URIINC_TRM[track.BL$CCT_URIINC_TRM==9]<- NA

track.BL$CCT_MACDEG_TRM[track.BL$CCT_MACDEG_TRM==1]<- 1 #Macular degeneration
track.BL$CCT_MACDEG_TRM[track.BL$CCT_MACDEG_TRM==2]<- 0
track.BL$CCT_MACDEG_TRM[track.BL$CCT_MACDEG_TRM==8]<- NA
track.BL$CCT_MACDEG_TRM[track.BL$CCT_MACDEG_TRM==9]<- NA

track.BL$CCT_CANC_TRM[track.BL$CCT_CANC_TRM==1]<- 1 #All-cause cancer
track.BL$CCT_CANC_TRM[track.BL$CCT_CANC_TRM==2]<- 0
track.BL$CCT_CANC_TRM[track.BL$CCT_CANC_TRM==8]<- NA
track.BL$CCT_CANC_TRM[track.BL$CCT_CANC_TRM==9]<- NA

track.BL$CCT_BCKP_TRM[track.BL$CCT_BCKP_TRM==1]<- 1 #Back problems but not fibromyalgia or arthritis
track.BL$CCT_BCKP_TRM[track.BL$CCT_BCKP_TRM==2]<- 0
track.BL$CCT_BCKP_TRM[track.BL$CCT_BCKP_TRM==8]<- NA
track.BL$CCT_BCKP_TRM[track.BL$CCT_BCKP_TRM==9]<- NA

track.BL$CCT_KIDN_TRM[track.BL$CCT_KIDN_TRM==1]<- 1 #Kidney disease
track.BL$CCT_KIDN_TRM[track.BL$CCT_KIDN_TRM==2]<- 0
track.BL$CCT_KIDN_TRM[track.BL$CCT_KIDN_TRM==8]<- NA
track.BL$CCT_KIDN_TRM[track.BL$CCT_KIDN_TRM==9]<- NA

track.BL$CCT_OTCCT_TRM[track.BL$CCT_OTCCT_TRM==1]<- 1 #Other long term mental or physical condition
track.BL$CCT_OTCCT_TRM[track.BL$CCT_OTCCT_TRM==2]<- 0
track.BL$CCT_OTCCT_TRM[track.BL$CCT_OTCCT_TRM==8]<- NA
track.BL$CCT_OTCCT_TRM[track.BL$CCT_OTCCT_TRM==9]<- NA

track.BL$CCT_OAHAND_TRM[track.BL$CCT_OAHAND_TRM==1]<- 1 #Hand arthritis
track.BL$CCT_OAHAND_TRM[track.BL$CCT_OAHAND_TRM==2]<- 0
track.BL$CCT_OAHAND_TRM[track.BL$CCT_OAHAND_TRM==8]<- NA
track.BL$CCT_OAHAND_TRM[track.BL$CCT_OAHAND_TRM==9]<- NA

track.BL$CCT_OAHIP_TRM[track.BL$CCT_OAHIP_TRM==1]<- 1 #Hip arthritis
track.BL$CCT_OAHIP_TRM[track.BL$CCT_OAHIP_TRM==2]<- 0
track.BL$CCT_OAHIP_TRM[track.BL$CCT_OAHIP_TRM==8]<- NA
track.BL$CCT_OAHIP_TRM[track.BL$CCT_OAHIP_TRM==9]<- NA

track.BL$CCT_OAKNEE_TRM[track.BL$CCT_OAKNEE_TRM==1]<- 1 #Knee arthritis
track.BL$CCT_OAKNEE_TRM[track.BL$CCT_OAKNEE_TRM==2]<- 0
track.BL$CCT_OAKNEE_TRM[track.BL$CCT_OAKNEE_TRM==8]<- NA
track.BL$CCT_OAKNEE_TRM[track.BL$CCT_OAKNEE_TRM==9]<- NA

track.BL$CCT_RA_TRM[track.BL$CCT_RA_TRM==1]<- 1 #Rheumatoid arthritis
track.BL$CCT_RA_TRM[track.BL$CCT_RA_TRM==2]<- 0
track.BL$CCT_RA_TRM[track.BL$CCT_RA_TRM==8]<- NA
track.BL$CCT_RA_TRM[track.BL$CCT_RA_TRM==9]<- NA

track.BL$CCT_OTART_TRM[track.BL$CCT_OTART_TRM==1]<- 1 #Other arthritis
track.BL$CCT_OTART_TRM[track.BL$CCT_OTART_TRM==2]<- 0
track.BL$CCT_OTART_TRM[track.BL$CCT_OTART_TRM==8]<- NA
track.BL$CCT_OTART_TRM[track.BL$CCT_OTART_TRM==9]<- NA

track.BL$CCT_DIAB_TRM[track.BL$CCT_DIAB_TRM==1]<- 1 #Diabetes
track.BL$CCT_DIAB_TRM[track.BL$CCT_DIAB_TRM==2]<- 0
track.BL$CCT_DIAB_TRM[track.BL$CCT_DIAB_TRM==8]<- NA
track.BL$CCT_DIAB_TRM[track.BL$CCT_DIAB_TRM==9]<- NA

track.BL$CCT_HBP_TRM[track.BL$CCT_HBP_TRM==1]<- 1 #High blood pressure
track.BL$CCT_HBP_TRM[track.BL$CCT_HBP_TRM==2]<- 0
track.BL$CCC_HBP_COM[track.BL$CCT_HBP_TRM==8]<- NA
track.BL$CCT_HBP_TRM[track.BL$CCT_HBP_TRM==9]<- NA

track.BL$CCT_UTHYR_TRM[track.BL$CCT_UTHYR_TRM==1]<- 1 #Under active thyroid
track.BL$CCT_UTHYR_TRM[track.BL$CCT_UTHYR_TRM==2]<- 0
track.BL$CCT_UTHYR_TRM[track.BL$CCT_UTHYR_TRM==8]<- NA
track.BL$CCT_UTHYR_TRM[track.BL$CCT_UTHYR_TRM==9]<- NA

track.BL$CCT_ANGI_TRM[track.BL$CCT_ANGI_TRM==1]<- 1 #Angina
track.BL$CCT_ANGI_TRM[track.BL$CCT_ANGI_TRM==2]<- 0
track.BL$CCT_ANGI_TRM[track.BL$CCT_ANGI_TRM==8]<- NA
track.BL$CCT_ANGI_TRM[track.BL$CCT_ANGI_TRM==9]<- NA

track.BL$CCT_CVA_TRM[track.BL$CCT_CVA_TRM==1]<- 1 #Stroke or CVA
track.BL$CCT_CVA_TRM[track.BL$CCT_CVA_TRM==2]<- 0
track.BL$CCT_CVA_TRM[track.BL$CCT_CVA_TRM==8]<- NA
track.BL$CCT_CVA_TRM[track.BL$CCT_CVA_TRM==9]<- NA

track.BL$CCT_AMI_TRM[track.BL$CCT_AMI_TRM==1]<- 1 #myocardial infarction
track.BL$CCT_AMI_TRM[track.BL$CCT_AMI_TRM==2]<- 0
track.BL$CCT_AMI_TRM[track.BL$CCT_AMI_TRM==8]<- NA
track.BL$CCT_AMI_TRM[track.BL$CCT_AMI_TRM==9]<- NA

track.BL$CCT_OTHYR_TRM[track.BL$CCT_OTHYR_TRM==1]<- 1 #Overactive thyroid
track.BL$CCT_OTHYR_TRM[track.BL$CCT_OTHYR_TRM==2]<- 0
track.BL$CCT_OTHYR_TRM[track.BL$CCT_OTHYR_TRM==8]<- NA
track.BL$CCT_OTHYR_TRM[track.BL$CCT_OTHYR_TRM==9]<- NA

track.BL$CCT_TIA_TRM[track.BL$CCT_TIA_TRM==1]<- 1 #Transient Ischemic Attack
track.BL$CCT_TIA_TRM[track.BL$CCT_TIA_TRM==2]<- 0
track.BL$CCT_TIA_TRM[track.BL$CCT_TIA_TRM==8]<- NA
track.BL$CCT_TIA_TRM[track.BL$CCT_TIA_TRM==9]<- NA

track.BL$CCT_ASTHM_TRM[track.BL$CCT_ASTHM_TRM==1]<- 1 #Asthma
track.BL$CCT_ASTHM_TRM[track.BL$CCT_ASTHM_TRM==2]<- 0
track.BL$CCT_ASTHM_TRM[track.BL$CCT_ASTHM_TRM==8]<- NA
track.BL$CCT_ASTHM_TRM[track.BL$CCT_ASTHM_TRM==9]<- NA

track.BL$CCT_OSTPO_TRM[track.BL$CCT_OSTPO_TRM==1]<- 1 #Osteoperosis
track.BL$CCT_OSTPO_TRM[track.BL$CCT_OSTPO_TRM==2]<- 0
track.BL$CCT_OSTPO_TRM[track.BL$CCT_OSTPO_TRM==8]<- NA
track.BL$CCT_OSTPO_TRM[track.BL$CCT_OSTPO_TRM==9]<- NA

track.BL$CCT_PARK_TRM[track.BL$CCT_PARK_TRM==1]<- 1 #Parkinsons
track.BL$CCT_PARK_TRM[track.BL$CCT_PARK_TRM==2]<- 0
track.BL$CCT_PARK_TRM[track.BL$CCT_PARK_TRM==8]<- NA
track.BL$CCT_PARK_TRM[track.BL$CCT_PARK_TRM==9]<- NA

track.BL$CCT_COPD_TRM[track.BL$CCT_COPD_TRM==1]<- 1 #COPD
track.BL$CCT_COPD_TRM[track.BL$CCT_COPD_TRM==2]<- 0
track.BL$CCT_COPD_TRM[track.BL$CCT_COPD_TRM==8]<- NA
track.BL$CCT_COPD_TRM[track.BL$CCT_COPD_TRM==9]<- NA

track.BL$Chronic_conditions<-track.BL$CCT_HEART_TRM + track.BL$CCT_PVD_TRM + track.BL$CCT_MEMPB_TRM + track.BL$CCT_ALZH_TRM + track.BL$CCT_MS_TRM + 
  track.BL$CCT_EPIL_TRM + track.BL$CCT_MGRN_TRM + track.BL$CCT_ULCR_TRM +
  track.BL$CCT_IBDIBS_TRM + track.BL$CCT_BOWINC_TRM + track.BL$CCT_URIINC_TRM + track.BL$CCT_MACDEG_TRM + track.BL$CCT_CANC_TRM + track.BL$CCT_BCKP_TRM + track.BL$CCT_KIDN_TRM + 
  track.BL$CCT_OTCCT_TRM + track.BL$CCT_OAHAND_TRM + track.BL$CCT_OAHIP_TRM + track.BL$CCT_OAKNEE_TRM + track.BL$CCT_RA_TRM + track.BL$CCT_OTART_TRM +
  track.BL$CCT_DIAB_TRM + track.BL$CCT_HBP_TRM + track.BL$CCT_UTHYR_TRM + track.BL$CCT_ANGI_TRM + track.BL$CCT_CVA_TRM + track.BL$CCT_AMI_TRM + track.BL$CCT_OTHYR_TRM + 
  track.BL$CCT_TIA_TRM + track.BL$CCT_ASTHM_TRM + track.BL$CCT_OSTPO_TRM + track.BL$CCT_PARK_TRM + track.BL$CCT_COPD_TRM

#Restless Sleep (≥ 3-4 days/week)
track.BL$RSTLS_Sleep<-NA
track.BL$RSTLS_Sleep[track.BL$DEP_RSTLS_TRM<3]<-1
track.BL$RSTLS_Sleep[track.BL$DEP_RSTLS_TRM>=3 & track.BL$DEP_RSTLS_TRM<8]<-0
track.BL$RSTLS_Sleep[track.BL$DEP_RSTLS_TRM>4]<-NA

#Finalize data set

track.BL.1<-track.BL[c(1,3,4,72:80,101:109,82,111)]

names(track.BL.1) <-paste(names(track.BL.1),"_0", sep="")

track.BL.Final<- rename(track.BL.1, "ID" = "entity_id_0")

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#Baseline (Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
track.BLcogs<-trackBL[c(1,1010,1011,1068,1055,1067,1065,1066,995,1058)]

############Cognitive Function##############

#~~~~~~Animal Fluency~~~~~~~~~~~#
track.BLcogs$Animal_Fluency_Lang<-NA
track.BLcogs$Animal_Fluency_Lang[track.BLcogs$COG_AFT_STARTLANG_TRM=="en"]<-"English"
track.BLcogs$Animal_Fluency_Lang[track.BLcogs$COG_AFT_STARTLANG_TRM=="fr"]<-"French"

track.BLcogs$Animal_Fluency_Strict<-track.BLcogs$COG_AFT_SCORE_1_TRM
track.BLcogs$Animal_Fluency_Lenient<-track.BLcogs$COG_AFT_SCORE_2_TRM



#~~~~~~~~Mental Alteration Test~~~~~~~~~~#
track.BLcogs$MAT_Lang<-NA
track.BLcogs$MAT_Lang[track.BLcogs$COG_MAT_STARTLANG_TRM=="en"]<-"English"
track.BLcogs$MAT_Lang[track.BLcogs$COG_MAT_STARTLANG_TRM=="fr"]<-"French"

track.BLcogs$MAT_Score<-track.BLcogs$COG_MAT_SCORE_TRM

#~~~~~~~~RVLT~~~~~~~~~~~~~~~~#
#Rey-Immediate Recall
track.BLcogs$RVLT_Immediate_Lang<- NA
track.BLcogs$RVLT_Immediate_Lang[track.BLcogs$COG_REYI_STARTLANG_TRM=="en"]<-"English"
track.BLcogs$RVLT_Immediate_Lang[track.BLcogs$COG_REYI_STARTLANG_TRM=="fr"]<-"French"

track.BLcogs$RVLT_Immediate_Score<-track.BLcogs$COG_REYI_SCORE_TRM

#Rey-Delayed Recall
track.BLcogs$RVLT_Delayed_Lang<- NA
track.BLcogs$RVLT_Delayed_Lang[track.BLcogs$COG_REYII_STARTLANG_TRM=="en"]<-"English"
track.BLcogs$RVLT_Delayed_Lang[track.BLcogs$COG_REYII_STARTLANG_TRM=="fr"]<-"French"

track.BLcogs$RVLT_Delayed_Score<-track.BLcogs$COG_REYII_SCORE_TRM

track.BLcogs1 <- track.BLcogs[c(1,11:19)]

names(track.BLcogs1) <-paste(names(track.BLcogs1),"_0", sep="")

track.BLcogs.Final<- rename(track.BLcogs1, "ID" = "entity_id_0")

1.2.2) Follow-up 1 processing

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#FU1 (Non-Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_FUP1")

trackFU1<-read.csv("/Users/ryan/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_FUP1/23SP001_McMaster_PRaina_FUP1_Trav3-1.csv")#Comprehensive Cohort Baseline

track.FU1<-trackFU1[c(1,75,76,594,83,1105,78,1859,134,155,174,181,187,204,201,210,223,228,240,
                      244,252,140,157,163,176,183,190,256,258,1832,1842,444,448,495,496,770,
                      423,434,451,575,455,458,461,462,463,470,473,514,523,537,399,396,392,401,
                      404,419,413,519,426,437,430,522,441,407,512,412,546,223,731)]

#Sex (no variable in FU1)
SexFU1 <- track.FU1 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id, Sex)
track.FU1$Sex<-SexFU1$Sex


#Age
track.FU1$Age<-track.FU1$AGE_NMBR_TRF1

#Marital Status
track.FU1$Relationship_status<-NA
track.FU1$Relationship_status[track.FU1$SDC_MRTL_TRF1==1]<-"Single"
track.FU1$Relationship_status[track.FU1$SDC_MRTL_TRF1==2]<-"Married"
track.FU1$Relationship_status[track.FU1$SDC_MRTL_TRF1==3]<-"Widowed"
track.FU1$Relationship_status[track.FU1$SDC_MRTL_TRF1==4]<-"Divorced"
track.FU1$Relationship_status[track.FU1$SDC_MRTL_TRF1==5]<-"Separated"

#Education 4 Category (No variable in FU1)
EducationFU1 <- track.FU1 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id,Education4)
track.FU1$Education4<-EducationFU1$Education4

#Household Income
track.FU1$Income_Level<-NA
track.FU1$Income_Level[track.FU1$INC_PTOT_TRF1==1]<-"<$20k"
track.FU1$Income_Level[track.FU1$INC_PTOT_TRF1==2]<-"$20-50k"
track.FU1$Income_Level[track.FU1$INC_PTOT_TRF1==3]<-"$50-100k"
track.FU1$Income_Level[track.FU1$INC_PTOT_TRF1==4]<-"$100-150k"
track.FU1$Income_Level[track.FU1$INC_PTOT_TRF1==5]<-">$150k"

#Living Status
track.FU1$Living_status<-NA
track.FU1$Living_status[track.FU1$OWN_DWLG_TRF1==1]<-"House"
track.FU1$Living_status[track.FU1$OWN_DWLG_TRF1==2 |track.FU1$OWN_DWLG_TRF1==6]<-"Apartment/Condo/Townhome"
track.FU1$Living_status[track.FU1$OWN_DWLG_TRF1==3]<-"Assisted Living"
track.FU1$Living_status[track.FU1$OWN_DWLG_TRF1==4 | track.FU1$OWN_DWLG_TRF1==5 | track.FU1$OWN_DWLG_TRF1>=7]<-"Other"

#Alcohol      
track.FU1$Alcohol<-NA
track.FU1$Alcohol[track.FU1$ALC_TTM_TRF1==1]<-"Regular drinker (at least once a month)"
track.FU1$Alcohol[track.FU1$ALC_TTM_TRF1==2]<-"Occasional drinker"
track.FU1$Alcohol[track.FU1$ALC_TTM_TRF1==3]<-"Non-drinker"

#Smoking Status (no variable in FU1)
SmokingFU1 <- track.FU1 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id,Smoking_Status)
track.FU1$Smoking_Status<-SmokingFU1$Smoking_Status

#Ethnicity (take from baseline)
EthnicityFU1 <- track.FU1 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id, Ethnicity)
track.FU1$Ethnicity<-EthnicityFU1$Ethnicity

#############Physical Activity Scale for the Elderly######################
#Q1: Sitting Activity Frequency in Past 7 days
track.FU1$PASE_Q1<-NA
track.FU1$PASE_Q1[track.FU1$PA2_SIT_TRF1==1]<-0
track.FU1$PASE_Q1[track.FU1$PA2_SIT_TRF1==2]<-0.11
track.FU1$PASE_Q1[track.FU1$PA2_SIT_TRF1==3]<-0.25
track.FU1$PASE_Q1[track.FU1$PA2_SIT_TRF1==4]<-0.43
track.FU1$PASE_Q1[track.FU1$PA2_SIT_TRF1>4]<-NA
track.FU1$PASE_Q1[is.na(track.FU1$PA2_SIT_TRF1)]<-NA

track.FU1$PASE_Q1B<-NA
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1==1]<-0
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1==2]<-1
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1==3]<-3
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1==4]<-6
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1==5]<-10
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1>5]<- 0
track.FU1$PASE_Q1B[is.na(track.FU1$PA2_SITHR_TRF1)]<-NA
track.FU1$PASE_Q1B<-as.numeric(track.FU1$PASE_Q1B)

#Q2: Walking outside frequency
track.FU1$PASE_Q2<- NULL
track.FU1$PASE_Q2[track.FU1$PA2_WALK_TRF1==1]<-0
track.FU1$PASE_Q2[track.FU1$PA2_WALK_TRF1==2]<-0.11
track.FU1$PASE_Q2[track.FU1$PA2_WALK_TRF1==3]<-0.25
track.FU1$PASE_Q2[track.FU1$PA2_WALK_TRF1==4]<-0.43
track.FU1$PASE_Q2[track.FU1$PA2_WALK_TRF1>4]<-NA
track.FU1$PASE_Q2[is.na(track.FU1$PA2_WALK_TRF1)]<-NA

track.FU1$PASE_Q2A<- NA
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1==1]<-0
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1==2]<-1
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1==3]<-3
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1==4]<-6
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1==5]<-10
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1>5]<-NA
track.FU1$PASE_Q2A[is.na(track.FU1$PA2_WALKHR_TRF1)]<-NA
track.FU1$PASE_Q2A<-as.numeric(track.FU1$PASE_Q2A)

#Q3: Light Sports or Activity Frequency
track.FU1$PASE_Q3<- NA
track.FU1$PASE_Q3[track.FU1$PA2_LSPRT_TRF1==1]<-0
track.FU1$PASE_Q3[track.FU1$PA2_LSPRT_TRF1==2]<-0.11
track.FU1$PASE_Q3[track.FU1$PA2_LSPRT_TRF1==3]<-0.25
track.FU1$PASE_Q3[track.FU1$PA2_LSPRT_TRF1==4]<-0.43
track.FU1$PASE_Q3[track.FU1$PA2_LSPRT_TRF1>4]<-NA
track.FU1$PASE_Q3[is.na(track.FU1$PA2_LSPRT_TRF1)]<-NA

track.FU1$PASE_Q3A<- NA
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1==1]<-0
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1==2]<-1
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1==3]<-3
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1==4]<-6
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1==5]<-10
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1>5]<-NA
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1 == 1 | is.na(track.FU1$PA2_LSPRTHR_TRF1)]<-0
track.FU1$PASE_Q3A[is.na(track.FU1$PA2_LSPRT_TRF1) & is.na(track.FU1$PA2_LSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1>4 & is.na(track.FU1$PA2_LSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q3A<-as.numeric(track.FU1$PASE_Q3A)

#Q4: Moderate Sports or Activity Frequency
track.FU1$PASE_Q4<-NA
track.FU1$PASE_Q4[track.FU1$PA2_MSPRT_TRF1==1]<-0
track.FU1$PASE_Q4[track.FU1$PA2_MSPRT_TRF1==2]<-0.11
track.FU1$PASE_Q4[track.FU1$PA2_MSPRT_TRF1==3]<-0.25
track.FU1$PASE_Q4[track.FU1$PA2_MSPRT_TRF1==4]<-0.43
track.FU1$PASE_Q4[track.FU1$PA2_MSPRT_TRF1>4]<-NA
track.FU1$PASE_Q4[is.na(track.FU1$PA2_MSPRT_TRF1)]<-NA

track.FU1$PASE_Q4A<- NA
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1==1]<-0
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1==2]<-1
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1==3]<-3
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1==4]<-6
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1==5]<-10
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1>5]<-NA
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRT_TRF1 == 1 | is.na(track.FU1$PA2_MSPRTHR_TRF1)]<-0
track.FU1$PASE_Q4A[is.na(track.FU1$PA2_MSPRT_TRF1) & is.na(track.FU1$PA2_MSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRT_TRF1>4 & is.na(track.FU1$PA2_MSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q4A<-as.numeric(track.FU1$PASE_Q4A)

#Q5: Strenuous Sports or Activity Frequency
track.FU1$PASE_Q5<-NA
track.FU1$PASE_Q5[track.FU1$PA2_SSPRT_TRF1==1]<-0
track.FU1$PASE_Q5[track.FU1$PA2_SSPRT_TRF1==2]<-0.11
track.FU1$PASE_Q5[track.FU1$PA2_SSPRT_TRF1==3]<-0.25
track.FU1$PASE_Q5[track.FU1$PA2_SSPRT_TRF1==4]<-0.43
track.FU1$PASE_Q5[track.FU1$PA2_SSPRT_TRF1>4]<-NA
track.FU1$PASE_Q5[is.na(track.FU1$PA2_SSPRT_TRF1)]<-NA

track.FU1$PASE_Q5A<- NA
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1==1]<-0
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1==2]<-1
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1==3]<-3
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1==4]<-6
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1==5]<-10
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1>5]<-NA
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRT_TRF1 == 1 | is.na(track.FU1$PA2_SSPRTHR_TRF1)]<-0
track.FU1$PASE_Q5A[is.na(track.FU1$PA2_SSPRT_TRF1) & is.na(track.FU1$PA2_SSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRT_TRF1>4 & is.na(track.FU1$PA2_SSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q5A<-as.numeric(track.FU1$PASE_Q5A)

#Q6: Muscle strengthening and endurance exercise
track.FU1$PASE_Q6<-NA
track.FU1$PASE_Q6[track.FU1$PA2_EXER_TRF1==1]<-0
track.FU1$PASE_Q6[track.FU1$PA2_EXER_TRF1==2]<-0.11
track.FU1$PASE_Q6[track.FU1$PA2_EXER_TRF1==3]<-0.25
track.FU1$PASE_Q6[track.FU1$PA2_EXER_TRF1==4]<-0.43
track.FU1$PASE_Q6[track.FU1$PA2_EXER_TRF1>4]<-NA
track.FU1$PASE_Q6[is.na(track.FU1$PA2_EXER_TRF1)]<-NA

track.FU1$PASE_Q6A<- NA
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1==1]<-0
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1==2]<-1
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1==3]<-3
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1==4]<-6
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1==5]<-10
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1>5]<-NA
track.FU1$PASE_Q6A[track.FU1$PA2_EXER_TRF1 == 1 | is.na(track.FU1$PA2_EXERHR_TRF1)]<-0
track.FU1$PASE_Q6A[is.na(track.FU1$PA2_EXER_TRF1) & is.na(track.FU1$PA2_EXERHR_TRF1)]<-NA
track.FU1$PASE_Q6A[track.FU1$PA2_EXER_TRF1>4 & is.na(track.FU1$PA2_EXERHR_TRF1)]<-NA
track.FU1$PASE_Q6A<-as.numeric(track.FU1$PASE_Q6A)

#Q7: Light Housework
track.FU1$PASE_Q7<-NA
track.FU1$PASE_Q7[track.FU1$PA2_LTHSWK_TRF1==1]<-1
track.FU1$PASE_Q7[track.FU1$PA2_LTHSWK_TRF1==2]<-0
track.FU1$PASE_Q7[track.FU1$PA2_LTHSWK_TRF1>2]<-NA
track.FU1$PASE_Q7[is.na(track.FU1$PA2_LTHSWK_TRF1)]<-NA
track.FU1$PASE_Q7<-as.numeric(track.FU1$PASE_Q7)

#Q8: Heavy Housework
track.FU1$PASE_Q8<-NA
track.FU1$PASE_Q8[track.FU1$PA2_HVYHSWK_TRF1==1]<-1
track.FU1$PASE_Q8[track.FU1$PA2_HVYHSWK_TRF1==2]<-0
track.FU1$PASE_Q8[track.FU1$PA2_HVYHSWK_TRF1>2]<-NA
track.FU1$PASE_Q8[is.na(track.FU1$PA2_HVYHSWK_TRF1)]<-NA
track.FU1$PASE_Q8<-as.numeric(track.FU1$PASE_Q8)

#Q9: Home Repair, Yardwork, Gardening, Care for another person
track.FU1$PASE_Q9A<-NA
track.FU1$PASE_Q9A[track.FU1$PA2_HMREPAIR_TRF1==1]<-1
track.FU1$PASE_Q9A[track.FU1$PA2_HMREPAIR_TRF1==2]<-0
track.FU1$PASE_Q9A[track.FU1$PA2_HMREPAIR_TRF1>2]<-NA
track.FU1$PASE_Q9A[is.na(track.FU1$PA2_HMREPAIR_TRF1)]<-NA
track.FU1$PASE_Q9A<-as.numeric(track.FU1$PASE_Q9A)

track.FU1$PASE_Q9B<-NA
track.FU1$PASE_Q9B[track.FU1$PA2_HVYODA_TRF1==1]<-1
track.FU1$PASE_Q9B[track.FU1$PA2_HVYODA_TRF1==2]<-0
track.FU1$PASE_Q9B[track.FU1$PA2_HVYODA_TRF1>2]<-NA
track.FU1$PASE_Q9B[is.na(track.FU1$PA2_HVYODA_TRF1)]<-NA
track.FU1$PASE_Q9B<-as.numeric(track.FU1$PASE_Q9B)

track.FU1$PASE_Q9C<-NA
track.FU1$PASE_Q9C[track.FU1$PA2_LTODA_TRF1==1]<-1
track.FU1$PASE_Q9C[track.FU1$PA2_LTODA_TRF1==2]<-0
track.FU1$PASE_Q9C[track.FU1$PA2_LTODA_TRF1>2]<-NA
track.FU1$PASE_Q9C[is.na(track.FU1$PA2_LTODA_TRF1)]<-NA
track.FU1$PASE_Q9C<-as.numeric(track.FU1$PASE_Q9C)

track.FU1$PASE_Q9D<-NA
track.FU1$PASE_Q9D[track.FU1$PA2_CRPRSN_TRF1==1]<-1
track.FU1$PASE_Q9D[track.FU1$PA2_CRPRSN_TRF1==2]<-0
track.FU1$PASE_Q9D[track.FU1$PA2_CRPRSN_TRF1>2]<-NA
track.FU1$PASE_Q9D[is.na(track.FU1$PA2_CRPRSN_TRF1)]<-NA
track.FU1$PASE_Q9D<-as.numeric(track.FU1$PASE_Q9D)

#Q10: Working and Volunteering
track.FU1$PASE_Q10<-NA
track.FU1$PASE_Q10[track.FU1$PA2_WRK_TRF1==1]<-1
track.FU1$PASE_Q10[track.FU1$PA2_WRK_TRF1==2]<-0
track.FU1$PASE_Q10[track.FU1$PA2_WRK_TRF1>2]<-NA
track.FU1$PASE_Q10[is.na(track.FU1$PA2_WRK_TRF1)]<-NA
track.FU1$PASE_Q10<-as.numeric(track.FU1$PASE_Q10)

track.FU1$PASE_Q10A<-NA
track.FU1$PASE_Q10A<-track.FU1$PA2_WRKHRS_NB_TRF1
track.FU1$PASE_Q10A[track.FU1$PA2_WRKHRS_NB_TRF1>=700]<-NA
track.FU1$PASE_Q10A[track.FU1$PA2_WRK_TRF1 == 2 | is.na(track.FU1$PA2_WRKHRS_NB_TRF1)]<-0
track.FU1$PASE_Q10A[is.na(track.FU1$PA2_WRK_TRF1) & is.na(track.FU1$PA2_WRKHRS_NB_TRF1)]<-NA
track.FU1$PASE_Q10A<-as.numeric(track.FU1$PASE_Q10A)
track.FU1$PASE_Q10A<-track.FU1$PASE_Q10A/7

#PASE TOTAL SCORE#
track.FU1$PASE_TOTAL<-track.FU1$PASE_Q2*track.FU1$PASE_Q2A*20 + track.FU1$PASE_Q3*track.FU1$PASE_Q3A*21 +  track.FU1$PASE_Q4*track.FU1$PASE_Q4A*23 + track.FU1$PASE_Q5*track.FU1$PASE_Q5A*30 +
  track.FU1$PASE_Q6*track.FU1$PASE_Q6A*30 + (track.FU1$PASE_Q7+track.FU1$PASE_Q8)*25 + track.FU1$PASE_Q9A*30 + track.FU1$PASE_Q9B*36 + track.FU1$PASE_Q9C*20 + track.FU1$PASE_Q9D*35 + 
  track.FU1$PASE_Q10*track.FU1$PASE_Q10A*21

#BMI
track.FU1$BMI<-track.FU1$HWT_DBMI_TRF1
track.FU1$BMI[track.FU1$HWT_DBMI_TRF1>100]<-NA

#CESD-10
track.FU1$CESD_10<-track.FU1$DEP_CESD10_TRF1
track.FU1$CESD_10[track.FU1$DEP_CESD10_TRF1==98]<-NA
track.FU1$CESD_10[track.FU1$DEP_CESD10_TRF1<0]<-NA

#Subjective Cognitive Impairment
track.FU1$SCI<- NA
track.FU1$SCI[track.FU1$CCT_MEMPB_TRF1==1]<- "Yes"
track.FU1$SCI[track.FU1$CCT_MEMPB_TRF1==2]<- "No"

#Dementia and AD
track.FU1$Dementia<- NA
track.FU1$Dementia[track.FU1$CCT_ALZH_TRF1==1]<- "Yes"
track.FU1$Dementia[track.FU1$CCT_ALZH_TRF1==2]<- "No"

#Anxiety
track.FU1$Anxiety<- NA
track.FU1$Anxiety[track.FU1$CCT_ANXI_TRF1==1]<- "Yes"
track.FU1$Anxiety[track.FU1$CCT_ANXI_TRF1==2]<- "No"

#Mood Disorders
track.FU1$Mood_Disord<- NA
track.FU1$Mood_Disord[track.FU1$CCT_MOOD_TRF1==1]<- "Yes"
track.FU1$Mood_Disord[track.FU1$CCT_MOOD_TRF1==2]<- "No"

#Pet Ownership at Baseline
track.FU1$Pet_Owner<-NA
track.FU1$Pet_Owner[track.FU1$SSA_PET_TRF1==1]<-"Yes"
track.FU1$Pet_Owner[track.FU1$SSA_PET_TRF1==2]<-"No"

#Number of Chronic Conditions
track.FU1$Chronic_conditions<-NA

track.FU1$CCT_HEART_TRM[track.FU1$CCT_HEART_TRF1==1]<- 1 #Heart Disease
track.FU1$CCT_HEART_TRM[track.FU1$CCT_HEART_TRF1==2]<- 0
track.FU1$CCT_HEART_TRM[track.FU1$CCT_HEART_TRF1==8]<- NA
track.FU1$CCT_HEART_TRM[track.FU1$CCT_HEART_TRF1==9]<- NA

track.FU1$CCT_PVD_TRM[track.FU1$CCT_PVD_TRF1==1]<- 1 #peripheral vascular disease
track.FU1$CCT_PVD_TRM[track.FU1$CCT_PVD_TRF1==2]<- 0
track.FU1$CCT_PVD_TRM[track.FU1$CCT_PVD_TRF1==8]<- NA
track.FU1$CCT_PVD_TRM[track.FU1$CCT_PVD_TRF1==9]<- NA

track.FU1$CCT_MEMPB_TRM[track.FU1$CCT_MEMPB_TRF1==1]<- 1 #SCI
track.FU1$CCT_MEMPB_TRM[track.FU1$CCT_MEMPB_TRF1==2]<- 0
track.FU1$CCT_MEMPB_TRM[track.FU1$CCT_MEMPB_TRF1==8]<- NA
track.FU1$CCT_MEMPB_TRM[track.FU1$CCT_MEMPB_TRF1==9]<- NA

track.FU1$CCT_ALZH_TRM[track.FU1$CCT_ALZH_TRF1==1]<- 1 #Alzheimers or demeinta
track.FU1$CCT_ALZH_TRM[track.FU1$CCT_ALZH_TRF1==2]<- 0
track.FU1$CCT_ALZH_TRM[track.FU1$CCT_ALZH_TRF1==8]<- NA
track.FU1$CCT_ALZH_TRM[track.FU1$CCT_ALZH_TRF1==9]<- NA

track.FU1$CCT_MS_TRM[track.FU1$CCT_MS_TRF1==1]<- 1 #Multiple sclerosis
track.FU1$CCT_MS_TRM[track.FU1$CCT_MS_TRF1==2]<- 0
track.FU1$CCT_MS_TRM[track.FU1$CCT_MS_TRF1==8]<- NA
track.FU1$CCT_MS_TRM[track.FU1$CCT_MS_TRF1==9]<- NA

track.FU1$CCT_EPIL_TRM[track.FU1$EPI_EVER_TRF1==1]<- 1 #Epilepsy
track.FU1$CCT_EPIL_TRM[track.FU1$EPI_EVER_TRF1==2]<- 0
track.FU1$CCT_EPIL_TRM[track.FU1$EPI_EVER_TRF1==8]<- NA
track.FU1$CCT_EPIL_TRM[track.FU1$EPI_EVER_TRF1==9]<- NA

track.FU1$CCT_MGRN_TRM[track.FU1$CCT_MGRN_TRF1==1]<- 1 #Migraine headaches
track.FU1$CCT_MGRN_TRM[track.FU1$CCT_MGRN_TRF1==2]<- 0
track.FU1$CCT_MGRN_TRM[track.FU1$CCT_MGRN_TRF1==8]<- NA
track.FU1$CCT_MGRN_TRM[track.FU1$CCT_MGRN_TRF1==9]<- NA

track.FU1$CCT_ULCR_TRM[track.FU1$CCT_ULCR_TRF1==1]<- 1 #Intenstinal or stomach ulcers
track.FU1$CCT_ULCR_TRM[track.FU1$CCT_ULCR_TRF1==2]<- 0
track.FU1$CCT_ULCR_TRM[track.FU1$CCT_ULCR_TRF1==8]<- NA
track.FU1$CCT_ULCR_TRM[track.FU1$CCT_ULCR_TRF1==9]<- NA

track.FU1$CCT_IBDIBS_TRM[track.FU1$CCT_IBDIBS_TRF1==1]<- 1 #Bowel disorder
track.FU1$CCT_IBDIBS_TRM[track.FU1$CCT_IBDIBS_TRF1==2]<- 0
track.FU1$CCT_IBDIBS_TRM[track.FU1$CCT_IBDIBS_TRF1==8]<- NA
track.FU1$CCT_IBDIBS_TRM[track.FU1$CCT_IBDIBS_TRF1==9]<- NA

track.FU1$CCT_BOWINC_TRM[track.FU1$CCT_BOWINC_TRF1==1]<- 1 #Bowel incontinence
track.FU1$CCT_BOWINC_TRM[track.FU1$CCT_BOWINC_TRF1==2]<- 0
track.FU1$CCT_BOWINC_TRM[track.FU1$CCT_BOWINC_TRF1==8]<- NA
track.FU1$CCT_BOWINC_TRM[track.FU1$CCT_BOWINC_TRF1==9]<- NA

track.FU1$CCT_URIINC_TRM[track.FU1$CCT_URIINC_TRF1==1]<- 1 #Urinary incontinence
track.FU1$CCT_URIINC_TRM[track.FU1$CCT_URIINC_TRF1==2]<- 0
track.FU1$CCT_URIINC_TRM[track.FU1$CCT_URIINC_TRF1==8]<- NA
track.FU1$CCT_URIINC_TRM[track.FU1$CCT_URIINC_TRF1==9]<- NA

track.FU1$CCT_MACDEG_TRM[track.FU1$CCT_MACDEG_TRF1==1]<- 1 #Macular degeneration
track.FU1$CCT_MACDEG_TRM[track.FU1$CCT_MACDEG_TRF1==2]<- 0
track.FU1$CCT_MACDEG_TRM[track.FU1$CCT_MACDEG_TRF1==8]<- NA
track.FU1$CCT_MACDEG_TRM[track.FU1$CCT_MACDEG_TRF1==9]<- NA

track.FU1$CCT_CANC_TRM[track.FU1$CCT_CANC_TRF1==1]<- 1 #All-cause cancer
track.FU1$CCT_CANC_TRM[track.FU1$CCT_CANC_TRF1==2]<- 0
track.FU1$CCT_CANC_TRM[track.FU1$CCT_CANC_TRF1==8]<- NA
track.FU1$CCT_CANC_TRM[track.FU1$CCT_CANC_TRF1==9]<- NA

track.FU1$CCT_BCKP_TRM[track.FU1$CCT_BCKP_TRF1==1]<- 1 #Back problems but not fibromyalgia or arthritis
track.FU1$CCT_BCKP_TRM[track.FU1$CCT_BCKP_TRF1==2]<- 0
track.FU1$CCT_BCKP_TRM[track.FU1$CCT_BCKP_TRF1==8]<- NA
track.FU1$CCT_BCKP_TRM[track.FU1$CCT_BCKP_TRF1==9]<- NA

track.FU1$CCT_KIDN_TRM[track.FU1$CCT_KIDN_TRF1==1]<- 1 #Kidney disease
track.FU1$CCT_KIDN_TRM[track.FU1$CCT_KIDN_TRF1==2]<- 0
track.FU1$CCT_KIDN_TRM[track.FU1$CCT_KIDN_TRF1==8]<- NA
track.FU1$CCT_KIDN_TRM[track.FU1$CCT_KIDN_TRF1==9]<- NA

track.FU1$CCT_OTCCT_TRM[track.FU1$CCT_OTCCT_TRF1==1]<- 1 #Other long term mental or physical condition
track.FU1$CCT_OTCCT_TRM[track.FU1$CCT_OTCCT_TRF1==2]<- 0
track.FU1$CCT_OTCCT_TRM[track.FU1$CCT_OTCCT_TRF1==8]<- NA
track.FU1$CCT_OTCCT_TRM[track.FU1$CCT_OTCCT_TRF1==9]<- NA

track.FU1$CCT_OAHAND_TRM[track.FU1$CCT_OAHAND_TRF1==1]<- 1 #Hand arthritis
track.FU1$CCT_OAHAND_TRM[track.FU1$CCT_OAHAND_TRF1==2]<- 0
track.FU1$CCT_OAHAND_TRM[track.FU1$CCT_OAHAND_TRF1==8]<- NA
track.FU1$CCT_OAHAND_TRM[track.FU1$CCT_OAHAND_TRF1==9]<- NA

track.FU1$CCT_OAHIP_TRM[track.FU1$CCT_OAHIP_TRF1==1]<- 1 #Hip arthritis
track.FU1$CCT_OAHIP_TRM[track.FU1$CCT_OAHIP_TRF1==2]<- 0
track.FU1$CCT_OAHIP_TRM[track.FU1$CCT_OAHIP_TRF1==8]<- NA
track.FU1$CCT_OAHIP_TRM[track.FU1$CCT_OAHIP_TRF1==9]<- NA

track.FU1$CCT_OAKNEE_TRM[track.FU1$CCT_OAKNEE_TRF1==1]<- 1 #Knee arthritis
track.FU1$CCT_OAKNEE_TRM[track.FU1$CCT_OAKNEE_TRF1==2]<- 0
track.FU1$CCT_OAKNEE_TRM[track.FU1$CCT_OAKNEE_TRF1==8]<- NA
track.FU1$CCT_OAKNEE_TRM[track.FU1$CCT_OAKNEE_TRF1==9]<- NA

track.FU1$CCT_RA_TRM[track.FU1$CCT_RA_TRF1==1]<- 1 #Rheumatoid arthritis
track.FU1$CCT_RA_TRM[track.FU1$CCT_RA_TRF1==2]<- 0
track.FU1$CCT_RA_TRM[track.FU1$CCT_RA_TRF1==8]<- NA
track.FU1$CCT_RA_TRM[track.FU1$CCT_RA_TRF1==9]<- NA

track.FU1$CCT_OTART_TRM[track.FU1$CCT_OTART_TRF1==1]<- 1 #Other arthritis
track.FU1$CCT_OTART_TRM[track.FU1$CCT_OTART_TRF1==2]<- 0
track.FU1$CCT_OTART_TRM[track.FU1$CCT_OTART_TRF1==8]<- NA
track.FU1$CCT_OTART_TRM[track.FU1$CCT_OTART_TRF1==9]<- NA

track.FU1$CCT_DIAB_TRM[track.FU1$CCT_DIAB_TRF1==1]<- 1 #Diabetes
track.FU1$CCT_DIAB_TRM[track.FU1$CCT_DIAB_TRF1==2]<- 0
track.FU1$CCT_DIAB_TRM[track.FU1$CCT_DIAB_TRF1==8]<- NA
track.FU1$CCT_DIAB_TRM[track.FU1$CCT_DIAB_TRF1==9]<- NA

track.FU1$CCT_HBP_TRM[track.FU1$CCT_HBP_TRF1==1]<- 1 #High blood pressure
track.FU1$CCT_HBP_TRM[track.FU1$CCT_HBP_TRF1==2]<- 0
track.FU1$CCT_HBP_TRM[track.FU1$CCT_HBP_TRF1==8]<- NA
track.FU1$CCT_HBP_TRM[track.FU1$CCT_HBP_TRF1==9]<- NA

track.FU1$CCT_UTHYR_TRM[track.FU1$CCT_UTHYR_TRF1==1]<- 1 #Under active thyroid
track.FU1$CCT_UTHYR_TRM[track.FU1$CCT_UTHYR_TRF1==2]<- 0
track.FU1$CCT_UTHYR_TRM[track.FU1$CCT_UTHYR_TRF1==8]<- NA
track.FU1$CCT_UTHYR_TRM[track.FU1$CCT_UTHYR_TRF1==9]<- NA

track.FU1$CCT_ANGI_TRM[track.FU1$CCT_ANGI_TRF1==1]<- 1 #Angina
track.FU1$CCT_ANGI_TRM[track.FU1$CCT_ANGI_TRF1==2]<- 0
track.FU1$CCT_ANGI_TRM[track.FU1$CCT_ANGI_TRF1==8]<- NA
track.FU1$CCT_ANGI_TRM[track.FU1$CCT_ANGI_TRF1==9]<- NA

track.FU1$CCT_CVA_TRM[track.FU1$CCT_CVA_TRF1==1]<- 1 #Stroke or CVA
track.FU1$CCT_CVA_TRM[track.FU1$CCT_CVA_TRF1==2]<- 0
track.FU1$CCT_CVA_TRM[track.FU1$CCT_CVA_TRF1==8]<- NA
track.FU1$CCT_CVA_TRM[track.FU1$CCT_CVA_TRF1==9]<- NA

track.FU1$CCT_AMI_TRM[track.FU1$CCT_AMI_TRF1==1]<- 1 #myocardial infarction
track.FU1$CCT_AMI_TRM[track.FU1$CCT_AMI_TRF1==2]<- 0
track.FU1$CCT_AMI_TRM[track.FU1$CCT_AMI_TRF1==8]<- NA
track.FU1$CCT_AMI_TRM[track.FU1$CCT_AMI_TRF1==9]<- NA

track.FU1$CCT_OTHYR_TRM[track.FU1$CCT_OTHYR_TRF1==1]<- 1 #Overactive thyroid
track.FU1$CCT_OTHYR_TRM[track.FU1$CCT_OTHYR_TRF1==2]<- 0
track.FU1$CCT_OTHYR_TRM[track.FU1$CCT_OTHYR_TRF1==8]<- NA
track.FU1$CCT_OTHYR_TRM[track.FU1$CCT_OTHYR_TRF1==9]<- NA

track.FU1$CCT_TIA_TRM[track.FU1$CCT_TIA_TRF1==1]<- 1 #Transient Ischemic Attack
track.FU1$CCT_TIA_TRM[track.FU1$CCT_TIA_TRF1==2]<- 0
track.FU1$CCT_TIA_TRM[track.FU1$CCT_TIA_TRF1==8]<- NA
track.FU1$CCT_TIA_TRM[track.FU1$CCT_TIA_TRF1==9]<- NA

track.FU1$CCT_ASTHM_TRM[track.FU1$CCT_ASTHM_TRF1==1]<- 1 #Asthma
track.FU1$CCT_ASTHM_TRM[track.FU1$CCT_ASTHM_TRF1==2]<- 0
track.FU1$CCT_ASTHM_TRM[track.FU1$CCT_ASTHM_TRF1==8]<- NA
track.FU1$CCT_ASTHM_TRM[track.FU1$CCT_ASTHM_TRF1==9]<- NA

track.FU1$CCT_OSTPO_TRM[track.FU1$CCT_OSTPO_TRF1==1]<- 1 #Osteoperosis
track.FU1$CCT_OSTPO_TRM[track.FU1$CCT_OSTPO_TRF1==2]<- 0
track.FU1$CCT_OSTPO_TRM[track.FU1$CCT_OSTPO_TRF1==8]<- NA
track.FU1$CCT_OSTPO_TRM[track.FU1$CCT_OSTPO_TRF1==9]<- NA

track.FU1$CCT_PARK_TRM[track.FU1$PKD_PARK_TRF1==1]<- 1 #Parkinsons
track.FU1$CCT_PARK_TRM[track.FU1$PKD_PARK_TRF1==2]<- 0
track.FU1$CCT_PARK_TRM[track.FU1$PKD_PARK_TRF1==8]<- NA
track.FU1$CCT_PARK_TRM[track.FU1$PKD_PARK_TRF1==9]<- NA

track.FU1$CCT_COPD_TRM[track.FU1$CCT_COPD_TRF1==1]<- 1 #COPD
track.FU1$CCT_COPD_TRM[track.FU1$CCT_COPD_TRF1==2]<- 0
track.FU1$CCT_COPD_TRM[track.FU1$CCT_COPD_TRF1==8]<- NA
track.FU1$CCT_COPD_TRM[track.FU1$CCT_COPD_TRF1==9]<- NA

track.FU1$Chronic_conditions<-track.FU1$CCT_HEART_TRM + track.FU1$CCT_PVD_TRM + track.FU1$CCT_MEMPB_TRM + track.FU1$CCT_ALZH_TRM + track.FU1$CCT_MS_TRM +
  track.FU1$CCT_EPIL_TRM + track.FU1$CCT_MGRN_TRM + track.FU1$CCT_ULCR_TRM +
  track.FU1$CCT_IBDIBS_TRM + track.FU1$CCT_BOWINC_TRM + track.FU1$CCT_URIINC_TRM + track.FU1$CCT_MACDEG_TRM + track.FU1$CCT_CANC_TRM + track.FU1$CCT_BCKP_TRM + track.FU1$CCT_KIDN_TRM + 
  track.FU1$CCT_OTCCT_TRM + track.FU1$CCT_OAHAND_TRM + track.FU1$CCT_OAHIP_TRM + track.FU1$CCT_OAKNEE_TRM + track.FU1$CCT_RA_TRM + track.FU1$CCT_OTART_TRM +
  track.FU1$CCT_DIAB_TRM + track.FU1$CCT_HBP_TRM + track.FU1$CCT_UTHYR_TRM + track.FU1$CCT_ANGI_TRM + track.FU1$CCT_CVA_TRM + track.FU1$CCT_AMI_TRM + track.FU1$CCT_OTHYR_TRM + 
  track.FU1$CCT_TIA_TRM + track.FU1$CCT_ASTHM_TRM + track.FU1$CCT_OSTPO_TRM + track.FU1$CCT_PARK_TRM + track.FU1$CCT_COPD_TRM

#Restless Sleep (≥ 3-4 days/week)
track.FU1$RSTLS_Sleep<-NA
track.FU1$RSTLS_Sleep[track.FU1$DEP_RSTLS_TRF1<3]<-1
track.FU1$RSTLS_Sleep[track.FU1$DEP_RSTLS_TRF1>=3 & track.FU1$DEP_RSTLS_TRF1<8]<-0
track.FU1$RSTLS_Sleep[track.FU1$DEP_RSTLS_TRF1>4]<-NA

#Finalize data set

track.FU1.1<-track.FU1[c(1:3,70:78,99:107,80,141)]

names(track.FU1.1) <-paste(names(track.FU1.1),"_1", sep="")

track.FU1.Final<- rename(track.FU1.1, "ID" = "entity_id_1")

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#FU1 (Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
track.FU1cogs<-trackFU1[c(1,1927,1928,1876,1875,1966,1877,1914,1878,1969)]

############Cognitive Function##############

#~~~~~~Animal Fluency~~~~~~~~~~~#
track.FU1cogs$Animal_Fluency_Lang<-NA
track.FU1cogs$Animal_Fluency_Lang[track.FU1cogs$COG_AFT_STARTLANG_TRF1=="en"]<-"English"
track.FU1cogs$Animal_Fluency_Lang[track.FU1cogs$COG_AFT_STARTLANG_TRF1=="fr"]<-"French"

track.FU1cogs$Animal_Fluency_Strict<-track.FU1cogs$COG_AFT_SCORE_1_TRF1
track.FU1cogs$Animal_Fluency_Strict[track.FU1cogs$Animal_Fluency_Strict<0]<-NA
track.FU1cogs$Animal_Fluency_Lenient<-track.FU1cogs$COG_AFT_SCORE_2_TRF1
track.FU1cogs$Animal_Fluency_Lenient[track.FU1cogs$Animal_Fluency_Lenient<0]<-NA



#~~~~~~~~Mental Alteration Test~~~~~~~~~~#
track.FU1cogs$MAT_Lang<-NA
track.FU1cogs$MAT_Lang[track.FU1cogs$COG_MAT_STARTLANG_TRF1=="en"]<-"English"
track.FU1cogs$MAT_Lang[track.FU1cogs$COG_MAT_STARTLANG_TRF1=="fr"]<-"French"

track.FU1cogs$MAT_Score<-track.FU1cogs$COG_MAT_SCORE_TRF1
track.FU1cogs$MAT_Score[track.FU1cogs$MAT_Score<0]<-NA

#~~~~~~~~RVLT~~~~~~~~~~~~~~~~#
#Rey-Immediate Recall
track.FU1cogs$RVLT_Immediate_Lang<- NA
track.FU1cogs$RVLT_Immediate_Lang[track.FU1cogs$COG_REYI_LANG_TRF1=="en"]<-"English"
track.FU1cogs$RVLT_Immediate_Lang[track.FU1cogs$COG_REYI_LANG_TRF1=="fr"]<-"French"

track.FU1cogs$RVLT_Immediate_Score<-track.FU1cogs$COG_REYI_SCORE_TRF1
track.FU1cogs$RVLT_Immediate_Score[track.FU1cogs$RVLT_Immediate_Score<0]<-NA


#Rey-Delayed Recall
track.FU1cogs$RVLT_Delayed_Lang<- NA
track.FU1cogs$RVLT_Delayed_Lang[track.FU1cogs$COG_REYII_LANG_TRF1=="en"]<-"English"
track.FU1cogs$RVLT_Delayed_Lang[track.FU1cogs$COG_REYII_LANG_TRF1=="fr"]<-"French"

track.FU1cogs$RVLT_Delayed_Score<-track.FU1cogs$COG_REYII_SCORE_TRF1
track.FU1cogs$RVLT_Delayed_Score[track.FU1cogs$RVLT_Delayed_Score<0]<-NA

track.FU1cogs1 <- track.FU1cogs[c(1,11:19)]

names(track.FU1cogs1) <-paste(names(track.FU1cogs1),"_1", sep="")

track.FU1cogs.Final<- rename(track.FU1cogs1, "ID" = "entity_id_1")

1.2.3) Follow-up 2 processing

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#FU2 (Non-Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_FUP2")

trackFU2<-read.csv("23SP001_McMaster_PRaina_FUP2_Trav1.csv")#Comprehensive Cohort Baseline

track.FU2<-trackFU2[c(1:5,47,1239,14,78,98,111,112,113,114,194,137,157,158,184,185,
                      191,195:202,50:54,685:694,415,419,514,517,732,389,401,423,604,427,
                      431,437,441,445,457,461,542,359,356,352,363,381,375,534,393,403,397,
                      538,407,367,530,371,568)]

#Sex (no variable in FU1)
SexFU2 <- track.FU2 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id, Sex)
track.FU2$Sex<-SexFU2$Sex

#Age
track.FU2$Age<-track.FU2$AGE_NMBR_TRF2

#Marital Status
track.FU2$Relationship_status<-NA
track.FU2$Relationship_status[track.FU2$SDC_MRTL_TRF2==1]<-"Single"
track.FU2$Relationship_status[track.FU2$SDC_MRTL_TRF2==2]<-"Married"
track.FU2$Relationship_status[track.FU2$SDC_MRTL_TRF2==3]<-"Widowed"
track.FU2$Relationship_status[track.FU2$SDC_MRTL_TRF2==4]<-"Divorced"
track.FU2$Relationship_status[track.FU2$SDC_MRTL_TRF2==5]<-"Separated"

#Education 4 Category (No variable in FU2)
EducationFU2 <- track.FU2 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id,Education4)
track.FU2$Education4<-EducationFU2$Education4

#Household Income
track.FU2$Income_Level<-NA
track.FU2$Income_Level[track.FU2$INC_PTOT_TRF2==1]<-"<$20k"
track.FU2$Income_Level[track.FU2$INC_PTOT_TRF2==2]<-"$20-50k"
track.FU2$Income_Level[track.FU2$INC_PTOT_TRF2==3]<-"$50-100k"
track.FU2$Income_Level[track.FU2$INC_PTOT_TRF2==4]<-"$100-150k"
track.FU2$Income_Level[track.FU2$INC_PTOT_TRF2==5]<-">$150k"

#Living Status
track.FU2$Living_status<-NA
track.FU2$Living_status[track.FU2$OWN_DWLG_TRF2==1]<-"House"
track.FU2$Living_status[track.FU2$OWN_DWLG_TRF2==2 |track.FU2$OWN_DWLG_TRF2==6]<-"Apartment/Condo/Townhome"
track.FU2$Living_status[track.FU2$OWN_DWLG_TRF2==3]<-"Assisted Living"
track.FU2$Living_status[track.FU2$OWN_DWLG_TRF2==4 | track.FU2$OWN_DWLG_TRF2==5 | track.FU2$OWN_DWLG_TRF2>=7]<-"Other"

#Alcohol (Based on a different question/scale)     
track.FU2$Alcohol<-NA
track.FU2$Alcohol[track.FU2$ALC_FREQ_TRF2<=6]<-"Regular drinker (at least once a month)"
track.FU2$Alcohol[track.FU2$ALC_FREQ_TRF2==7]<-"Occasional drinker"
track.FU2$Alcohol[track.FU2$ALC_FREQ_TRF2==96]<-"Non-drinker"

#Smoking Status (no variable in FU2)
SmokingFU2 <- track.FU2 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id,Smoking_Status)
track.FU2$Smoking_Status<-SmokingFU2$Smoking_Status

#Ethnicity (take from baseline))
EthnicityFU2 <- track.FU2 %>%
  left_join(track.BL, by = "entity_id") %>%
  select(entity_id,Ethnicity)
track.FU2$Ethnicity<-EthnicityFU2$Ethnicity

#############Physical Activity Scale for the Elderly######################
#Q1: Sitting Activity Frequency in Past 7 days
track.FU2$PASE_Q1<-NA
track.FU2$PASE_Q1[track.FU2$PA2_SIT_TRF2==1]<-0
track.FU2$PASE_Q1[track.FU2$PA2_SIT_TRF2==2]<-0.11
track.FU2$PASE_Q1[track.FU2$PA2_SIT_TRF2==3]<-0.25
track.FU2$PASE_Q1[track.FU2$PA2_SIT_TRF2==4]<-0.43
track.FU2$PASE_Q1[track.FU2$PA2_SIT_TRF2>4]<-NA
track.FU2$PASE_Q1[is.na(track.FU2$PA2_SIT_TRF2)]<-NA

track.FU2$PASE_Q1B<-NA
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2==1]<-0
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2==2]<-1
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2==3]<-3
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2==4]<-6
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2==5]<-10
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2>5]<- 0
track.FU2$PASE_Q1B[is.na(track.FU2$PA2_SITHR_SIT_TRF2)]<-NA
track.FU2$PASE_Q1B<-as.numeric(track.FU2$PASE_Q1B)

#Q2: Walking outside frequency
track.FU2$PASE_Q2<- NULL
track.FU2$PASE_Q2[track.FU2$PA2_WALK_TRF2==1]<-0
track.FU2$PASE_Q2[track.FU2$PA2_WALK_TRF2==2]<-0.11
track.FU2$PASE_Q2[track.FU2$PA2_WALK_TRF2==3]<-0.25
track.FU2$PASE_Q2[track.FU2$PA2_WALK_TRF2==4]<-0.43
track.FU2$PASE_Q2[track.FU2$PA2_WALK_TRF2>4]<-NA
track.FU2$PASE_Q2[is.na(track.FU2$PA2_WALK_TRF2)]<-NA

track.FU2$PASE_Q2A<- NA
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2==1]<-0
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2==2]<-1
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2==3]<-3
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2==4]<-6
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2==5]<-10
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2>5]<-NA
track.FU2$PASE_Q2A[is.na(track.FU2$PA2_WALKHR_TRF2)]<-NA
track.FU2$PASE_Q2A<-as.numeric(track.FU2$PASE_Q2A)

#Q3: Light Sports or Activity Frequency
track.FU2$PASE_Q3<- NA
track.FU2$PASE_Q3[track.FU2$PA2_LSPRT_TRF2==1]<-0
track.FU2$PASE_Q3[track.FU2$PA2_LSPRT_TRF2==2]<-0.11
track.FU2$PASE_Q3[track.FU2$PA2_LSPRT_TRF2==3]<-0.25
track.FU2$PASE_Q3[track.FU2$PA2_LSPRT_TRF2==4]<-0.43
track.FU2$PASE_Q3[track.FU2$PA2_LSPRT_TRF2>4]<-NA
track.FU2$PASE_Q3[is.na(track.FU2$PA2_LSPRT_TRF2)]<-NA

track.FU2$PASE_Q3A<- NA
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2==1]<-0
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2==2]<-1
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2==3]<-3
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2==4]<-6
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2==5]<-10
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2>5]<-NA
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2 == 1 | is.na(track.FU2$PA2_LSPRTHR_TRF2)]<-0
track.FU2$PASE_Q3A[is.na(track.FU2$PA2_LSPRTHR_TRF2) & is.na(track.FU2$PA2_LSPRTHR_TRF2)]<-NA
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2>4 & is.na(track.FU2$PA2_LSPRTHR_TRF2)]<-NA
track.FU2$PASE_3A<-as.numeric(track.FU2$PASE_Q3A)

#Q4: Moderate Sports or Activity Frequency
track.FU2$PASE_Q4<-NA
track.FU2$PASE_Q4[track.FU2$PA2_MSPRT_TRF2==1]<-0
track.FU2$PASE_Q4[track.FU2$PA2_MSPRT_TRF2==2]<-0.11
track.FU2$PASE_Q4[track.FU2$PA2_MSPRT_TRF2==3]<-0.25
track.FU2$PASE_Q4[track.FU2$PA2_MSPRT_TRF2==4]<-0.43
track.FU2$PASE_Q4[track.FU2$PA2_MSPRT_TRF2>4]<-NA
track.FU2$PASE_Q4[is.na(track.FU2$PA2_MSPRT_TRF2)]<-NA

track.FU2$PASE_Q4A<- NA
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2==1]<-0
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2==2]<-1
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2==3]<-3
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2==4]<-6
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2==5]<-10
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2>5]<-NA
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRT_TRF2 == 1 | is.na(track.FU2$PA2_MSPRTHR_TRF2)]<-0
track.FU2$PASE_Q4A[is.na(track.FU2$PA2_MSPRT_TRF2) & is.na(track.FU2$PA2_MSPRTHR_TRF2)]<-NA
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRT_TRF2>4 & is.na(track.FU2$PA2_MSPRTHR_TRF2)]<-NA
track.FU2$PASE_Q4A<-as.numeric(track.FU2$PASE_Q4A)

#Q5: Strenuous Sports or Activity Frequency
track.FU2$PASE_Q5<-NA
track.FU2$PASE_Q5[track.FU2$PA2_SSPRT_TRF2==1]<-0
track.FU2$PASE_Q5[track.FU2$PA2_SSPRT_TRF2==2]<-0.11
track.FU2$PASE_Q5[track.FU2$PA2_SSPRT_TRF2==3]<-0.25
track.FU2$PASE_Q5[track.FU2$PA2_SSPRT_TRF2==4]<-0.43
track.FU2$PASE_Q5[track.FU2$PA2_SSPRT_TRF2>4]<-NA
track.FU2$PASE_Q5[is.na(track.FU2$PA2_SSPRT_TRF2)]<-NA

track.FU2$PASE_Q5A<- NA
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2==1]<-0
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2==2]<-1
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2==3]<-3
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2==4]<-6
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2==5]<-10
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2>5]<-NA
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRT_TRF2 == 1 | is.na(track.FU2$PA2_SSPRTHR_TRF2)]<-0
track.FU2$PASE_Q5A[is.na(track.FU2$PA2_SSPRT_TRF2) & is.na(track.FU2$PA2_SSPRTHR_TRF2)]<-NA
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRT_TRF2>4 & is.na(track.FU2$PA2_SSPRTHR_TRF2)]<-NA
track.FU2$PASE_Q5A<-as.numeric(track.FU2$PASE_Q5A)

#Q6: Muscle strengthening and endurance exercise
track.FU2$PASE_Q6<-NA
track.FU2$PASE_Q6[track.FU2$PA2_EXER_TRF2==1]<-0
track.FU2$PASE_Q6[track.FU2$PA2_EXER_TRF2==2]<-0.11
track.FU2$PASE_Q6[track.FU2$PA2_EXER_TRF2==3]<-0.25
track.FU2$PASE_Q6[track.FU2$PA2_EXER_TRF2==4]<-0.43
track.FU2$PASE_Q6[track.FU2$PA2_EXER_TRF2>4]<-NA
track.FU2$PASE_Q6[is.na(track.FU2$PA2_EXER_TRF2)]<-NA

track.FU2$PASE_Q6A<- NA
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2==1]<-0
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2==2]<-1
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2==3]<-3
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2==4]<-6
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2==5]<-10
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2>5]<-NA
track.FU2$PASE_Q6A[track.FU2$PA2_EXER_TRF2 == 1 | is.na(track.FU2$PA2_EXERHR_TRF2)]<-0
track.FU2$PASE_Q6A[is.na(track.FU2$PA2_EXER_TRF2) & is.na(track.FU2$PA2_EXERHR_TRF2)]<-NA
track.FU2$PASE_Q6A[track.FU2$PA2_EXER_TRF2>4 & is.na(track.FU2$PA2_EXERHR_TRF2)]<-NA
track.FU2$PASE_Q6A<-as.numeric(track.FU2$PASE_Q6A)

#Q7: Light Housework
track.FU2$PASE_Q7<-NA
track.FU2$PASE_Q7[track.FU2$PA2_LTHSWK_TRF2==1]<-1
track.FU2$PASE_Q7[track.FU2$PA2_LTHSWK_TRF2==2]<-0
track.FU2$PASE_Q7[track.FU2$PA2_LTHSWK_TRF2>2]<-NA
track.FU2$PASE_Q7[is.na(track.FU2$PA2_LTHSWK_TRF2)]<-NA
track.FU2$PASE_Q7<-as.numeric(track.FU2$PASE_Q7)

#Q8: Heavy Housework
track.FU2$PASE_Q8<-NA
track.FU2$PASE_Q8[track.FU2$PA2_HVYHSWK_TRF2==1]<-1
track.FU2$PASE_Q8[track.FU2$PA2_HVYHSWK_TRF2==2]<-0
track.FU2$PASE_Q8[track.FU2$PA2_HVYHSWK_TRF2>2]<-NA
track.FU2$PASE_Q8[is.na(track.FU2$PA2_HVYHSWK_TRF2)]<-NA
track.FU2$PASE_Q8<-as.numeric(track.FU2$PASE_Q8)

#Q9: Home Repair, Yardwork, Gardening, Care for another person
track.FU2$PASE_Q9A<-NA
track.FU2$PASE_Q9A[track.FU2$PA2_HMREPAIR_TRF2==1]<-1
track.FU2$PASE_Q9A[track.FU2$PA2_HMREPAIR_TRF2==2]<-0
track.FU2$PASE_Q9A[track.FU2$PA2_HMREPAIR_TRF2>2]<-NA
track.FU2$PASE_Q9A[is.na(track.FU2$PA2_HMREPAIR_TRF2)]<-NA
track.FU2$PASE_Q9A<-as.numeric(track.FU2$PASE_Q9A)

track.FU2$PASE_Q9B<-NA
track.FU2$PASE_Q9B[track.FU2$PA2_HVYODA_TRF2==1]<-1
track.FU2$PASE_Q9B[track.FU2$PA2_HVYODA_TRF2==2]<-0
track.FU2$PASE_Q9B[track.FU2$PA2_HVYODA_TRF2>2]<-NA
track.FU2$PASE_Q9B[is.na(track.FU2$PA2_HVYODA_TRF2)]<-NA
track.FU2$PASE_Q9B<-as.numeric(track.FU2$PASE_Q9B)

track.FU2$PASE_Q9C<-NA
track.FU2$PASE_Q9C[track.FU2$PA2_LTODA_TRF2==1]<-1
track.FU2$PASE_Q9C[track.FU2$PA2_LTODA_TRF2==2]<-0
track.FU2$PASE_Q9C[track.FU2$PA2_LTODA_TRF2>2]<-NA
track.FU2$PASE_Q9C[is.na(track.FU2$PA2_LTODA_TRF2)]<-NA
track.FU2$PASE_Q9C<-as.numeric(track.FU2$PASE_Q9C)

track.FU2$PASE_Q9D<-NA
track.FU2$PASE_Q9D[track.FU2$PA2_CRPRSN_TRF2==1]<-1
track.FU2$PASE_Q9D[track.FU2$PA2_CRPRSN_TRF2==2]<-0
track.FU2$PASE_Q9D[track.FU2$PA2_CRPRSN_TRF2>2]<-NA
track.FU2$PASE_Q9D[is.na(track.FU2$PA2_CRPRSN_TRF2)]<-NA
track.FU2$PASE_Q9D<-as.numeric(track.FU2$PASE_Q9D)

#Q10: Working and Volunteering
track.FU2$PASE_Q10<-NA
track.FU2$PASE_Q10[track.FU2$PA2_WRK_TRF2==1]<-1
track.FU2$PASE_Q10[track.FU2$PA2_WRK_TRF2==2]<-0
track.FU2$PASE_Q10[track.FU2$PA2_WRK_TRF2>2]<-NA
track.FU2$PASE_Q10[is.na(track.FU2$PA2_WRK_TRF2)]<-NA
track.FU2$PASE_Q10<-as.numeric(track.FU2$PASE_Q10)

track.FU2$PASE_Q10A<-NA
track.FU2$PASE_Q10A<-track.FU2$PA2_WRKHRS_NB_TRF2
track.FU2$PASE_Q10A[track.FU2$PA2_WRKHRS_NB_TRF2>=700]<-NA
track.FU2$PASE_Q10A[track.FU2$PA2_WRK_TRF2 == 2 | is.na(track.FU2$PA2_WRKHRS_NB_TRF2)]<-0
track.FU2$PASE_Q10A[is.na(track.FU2$PA2_WRK_TRF2) & is.na(track.FU2$PA2_WRKHRS_NB_TRF2)]<-NA
track.FU2$PASE_Q10A<-as.numeric(track.FU2$PASE_Q10A)
track.FU2$PASE_Q10A<-track.FU2$PASE_Q10A/7

#PASE TOTAL SCORE#
track.FU2$PASE_TOTAL<-track.FU2$PASE_Q2*track.FU2$PASE_Q2A*20 + track.FU2$PASE_Q3*track.FU2$PASE_Q3A*21 +  track.FU2$PASE_Q4*track.FU2$PASE_Q4A*23 + track.FU2$PASE_Q5*track.FU2$PASE_Q5A*30 +
  track.FU2$PASE_Q6*track.FU2$PASE_Q6A*30 + (track.FU2$PASE_Q7+track.FU2$PASE_Q8)*25 + track.FU2$PASE_Q9A*30 + track.FU2$PASE_Q9B*36 + track.FU2$PASE_Q9C*20 + track.FU2$PASE_Q9D*35 + 
  track.FU2$PASE_Q10*track.FU2$PASE_Q10A*21


#BMI (Not included)
track.FU2$Height<-NA
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==1]<-36
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==2]<-37
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==3]<-38
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==4]<-39
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==5]<-40
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==6]<-41
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==7]<-42
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==8]<-43
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==9]<-44
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==10]<-45
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==11]<-46
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==12]<-47
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==1]<-48
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==2]<-49
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==3]<-50
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==4]<-51
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==5]<-52
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==6]<-53
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==7]<-54
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==8]<-55
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==9]<-56
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==10]<-57
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==11]<-58
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==12]<-59
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==1]<-60
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==2]<-61
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==3]<-62
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==4]<-63
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==5]<-64
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==6]<-65
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==7]<-66
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==8]<-67
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==9]<-68
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==10]<-69
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==11]<-70
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==12]<-71
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==1]<-72
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==2]<-73
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==3]<-74
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==4]<-75
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==5]<-76
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==6]<-77
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==7]<-78
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==8]<-79
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==9]<-80
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==10]<-81
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==11]<-82
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==12]<-83
track.FU2$Height<-as.numeric(track.FU2$Height)*0.0254

track.FU2$Weight<-track.FU2$HWT_WGHT_NB_TRF2
track.FU2$Weight[track.FU2$HWT_WGHT_NB_TRF2>500]<-NA
track.FU2$Weight[track.FU2$HWT_WGHT_NB_TRF2==-8888]<-NA

track.FU2$BMI<-track.FU2$Weight/((track.FU2$Height)^2)

#CESD-10 (NO CESD variable in the data set; need to calculate by hand)
#Q1: I was bothered by things that usually don’t bother me.
track.FU2$CESD_Q1<-NA
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2==1]<-3
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2==2]<-2
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2==3]<-1
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2==4]<-0
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2>4]<-NA
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2<0]<-NA

#Q2: I had trouble keeping my mind on what I was doing.
track.FU2$CESD_Q2<-NA
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2==1]<-3
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2==2]<-2
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2==3]<-1
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2==4]<-0
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2>4]<-NA
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2<0]<-NA

#Q3: I felt depressed
track.FU2$CESD_Q3<-NA
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2==1]<-3
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2==2]<-2
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2==3]<-1
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2==4]<-0
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2>4]<-NA
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2<0]<-NA

#Q4: I felt that everything I did was an effort.
track.FU2$CESD_Q4<-NA
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2==1]<-3
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2==2]<-2
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2==3]<-1
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2==4]<-0
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2>4]<-NA
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2<0]<-NA

#Q5: I felt hopeful about the future
track.FU2$CESD_Q5<-NA
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2==1]<-0
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2==2]<-1
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2==3]<-2
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2==4]<-3
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2>4]<-NA
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2<0]<-NA

#Q6: I felt fearful.
track.FU2$CESD_Q6<-NA
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2==1]<-3
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2==2]<-2
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2==3]<-1
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2==4]<-0
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2>4]<-NA
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2<0]<-NA

#Q7: My sleep was restless
track.FU2$CESD_Q7<-NA
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2==1]<-3
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2==2]<-2
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2==3]<-1
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2==4]<-0
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2>4]<-NA
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2<0]<-NA

#Q8: I was happy
track.FU2$CESD_Q8<-NA
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2==1]<-0
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2==2]<-1
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2==3]<-2
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2==4]<-3
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2>4]<-NA
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2<0]<-NA

#Q9: I felt lonely
track.FU2$CESD_Q9<-NA
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2==1]<-3
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2==2]<-2
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2==3]<-1
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2==4]<-0
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2>4]<-NA
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2<0]<-NA

#Q10: I could not “get going.”
track.FU2$CESD_Q10<-NA
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2==1]<-3
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2==2]<-2
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2==3]<-1
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2==4]<-0
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2>4]<-NA
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2<0]<-NA

track.FU2$CESD_10<-track.FU2$CESD_Q1 + track.FU2$CESD_Q2 + track.FU2$CESD_Q3 + track.FU2$CESD_Q4 + track.FU2$CESD_Q5 + 
  track.FU2$CESD_Q6 + track.FU2$CESD_Q7 + track.FU2$CESD_Q8 + track.FU2$CESD_Q9 + track.FU2$CESD_Q10

#Subjective Cognitive Impairment
track.FU2$SCI<- NA
track.FU2$SCI[track.FU2$CCT_MEMPB_TRF2==1]<- "Yes"
track.FU2$SCI[track.FU2$CCT_MEMPB_TRF2==2]<- "No"

#Dementia and AD
track.FU2$Dementia<- NA
track.FU2$Dementia[track.FU2$CCT_ALZH_TRF2==1]<- "Yes"
track.FU2$Dementia[track.FU2$CCT_ALZH_TRF2==2]<- "No"

#Anxiety
track.FU2$Anxiety<- NA
track.FU2$Anxiety[track.FU2$CCT_ANXI_TRF2==1]<- "Yes"
track.FU2$Anxiety[track.FU2$CCT_ANXI_TRF2==2]<- "No"

#Mood Disorders
track.FU2$Mood_Disord<- NA
track.FU2$Mood_Disord[track.FU2$CCT_MOOD_TRF2==1]<- "Yes"
track.FU2$Mood_Disord[track.FU2$CCT_MOOD_TRF2==2]<- "No"

#Number of Chronic Conditions
track.FU2$Chronic_conditions<-NA

#Pet Ownership at Baseline
track.FU2$Pet_Owner<-NA
track.FU2$Pet_Owner[track.FU2$SSA_PET_TRF2==1]<-"Yes"
track.FU2$Pet_Owner[track.FU2$SSA_PET_TRF2==2]<-"No"


track.FU2$CCT_HEART_TRM[track.FU2$CCT_HEART_TRF2==1]<- 1 #Heart Disease
track.FU2$CCT_HEART_TRM[track.FU2$CCT_HEART_TRF2==2]<- 0
track.FU2$CCT_HEART_TRM[track.FU2$CCT_HEART_TRF2==8]<- NA
track.FU2$CCT_HEART_TRM[track.FU2$CCT_HEART_TRF2==9]<- NA

track.FU2$CCT_PVD_TRM[track.FU2$CCT_PAD_TRF2==1]<- 1 #peripheral vascular disease (called Peripheral Artery Disease, unlike Baseline or FU1)
track.FU2$CCT_PVD_TRM[track.FU2$CCT_PAD_TRF2==2]<- 0
track.FU2$CCT_PVD_TRM[track.FU2$CCT_PAD_TRF2==8]<- NA
track.FU2$CCT_PVD_TRM[track.FU2$CCT_PAD_TRF2==9]<- NA

track.FU2$CCT_MEMPB_TRM[track.FU2$CCT_MEMPB_TRF2==1]<- 1 #SCI
track.FU2$CCT_MEMPB_TRM[track.FU2$CCT_MEMPB_TRF2==2]<- 0
track.FU2$CCT_MEMPB_TRM[track.FU2$CCT_MEMPB_TRF2==8]<- NA
track.FU2$CCT_MEMPB_TRM[track.FU2$CCT_MEMPB_TRF2==9]<- NA

track.FU2$CCT_ALZH_TRM[track.FU2$CCT_ALZH_TRF2==1]<- 1 #Alzheimers or demeinta
track.FU2$CCT_ALZH_TRM[track.FU2$CCT_ALZH_TRF2==2]<- 0
track.FU2$CCT_ALZH_TRM[track.FU2$CCT_ALZH_TRF2==8]<- NA
track.FU2$CCT_ALZH_TRM[track.FU2$CCT_ALZH_TRF2==9]<- NA

track.FU2$CCT_MS_TRM[track.FU2$CCT_MS_TRF2==1]<- 1 #Multiple sclerosis
track.FU2$CCT_MS_TRM[track.FU2$CCT_MS_TRF2==2]<- 0
track.FU2$CCT_MS_TRM[track.FU2$CCT_MS_TRF2==8]<- NA
track.FU2$CCT_MS_TRM[track.FU2$CCT_MS_TRF2==9]<- NA

track.FU2$CCT_EPIL_TRM[track.FU2$EPI_EVER_TRF2==1]<- 1 #Epilepsy (Different question)
track.FU2$CCT_EPIL_TRM[track.FU2$EPI_EVER_TRF2==2]<- 0
track.FU2$CCT_EPIL_TRM[track.FU2$EPI_EVER_TRF2==3]<- NA
track.FU2$CCT_EPIL_TRM[track.FU2$EPI_EVER_TRF2==8]<- NA
track.FU2$CCT_EPIL_TRM[track.FU2$EPI_EVER_TRF2==9]<- NA

track.FU2$CCT_MGRN_TRM[track.FU2$CCT_MGRN_TRF2==1]<- 1 #Migraine headaches
track.FU2$CCT_MGRN_TRM[track.FU2$CCT_MGRN_TRF2==2]<- 0
track.FU2$CCT_MGRN_TRM[track.FU2$CCT_MGRN_TRF2==8]<- NA
track.FU2$CCT_MGRN_TRM[track.FU2$CCT_MGRN_TRF2==9]<- NA

track.FU2$CCT_ULCR_TRM[track.FU2$CCT_ULCR_TRF2==1]<- 1 #Intenstinal or stomach ulcers
track.FU2$CCT_ULCR_TRM[track.FU2$CCT_ULCR_TRF2==2]<- 0
track.FU2$CCT_ULCR_TRM[track.FU2$CCT_ULCR_TRF2==8]<- NA
track.FU2$CCT_ULCR_TRM[track.FU2$CCT_ULCR_TRF2==9]<- NA

track.FU2$CCT_IBDIBS_TRM[track.FU2$CCT_IBSYD_TRF2==1]<- 1 #Bowel disorder (different name for IBS and IBD)
track.FU2$CCT_IBDIBS_TRM[track.FU2$CCT_IBSYD_TRF2==2]<- 0
track.FU2$CCT_IBDIBS_TRM[track.FU2$CCT_IBSYD_TRF2==8]<- NA
track.FU2$CCT_IBDIBS_TRM[track.FU2$CCT_IBSYD_TRF2==9]<- NA

track.FU2$CCT_BOWINC_TRM[track.FU2$CCT_BOWINC_TRF2==1]<- 1 #Bowel incontinence
track.FU2$CCT_BOWINC_TRM[track.FU2$CCT_BOWINC_TRF2==2]<- 0
track.FU2$CCT_BOWINC_TRM[track.FU2$CCT_BOWINC_TRF2==8]<- NA
track.FU2$CCT_BOWINC_TRM[track.FU2$CCT_BOWINC_TRF2==9]<- NA

track.FU2$CCT_URIINC_TRM[track.FU2$CCT_URIINC_TRF2==1]<- 1 #Urinary incontinence
track.FU2$CCT_URIINC_TRM[track.FU2$CCT_URIINC_TRF2==2]<- 0
track.FU2$CCT_URIINC_TRM[track.FU2$CCT_URIINC_TRF2==8]<- NA
track.FU2$CCT_URIINC_TRM[track.FU2$CCT_URIINC_TRF2==9]<- NA

track.FU2$CCT_MACDEG_TRM[track.FU2$CCT_MACDEG_TRF2==1]<- 1 #Macular degeneration
track.FU2$CCT_MACDEG_TRM[track.FU2$CCT_MACDEG_TRF2==2]<- 0
track.FU2$CCT_MACDEG_TRM[track.FU2$CCT_MACDEG_TRF2==8]<- NA
track.FU2$CCT_MACDEG_TRM[track.FU2$CCT_MACDEG_TRF2==9]<- NA

track.FU2$CCT_CANC_TRM[track.FU2$CCT_CANC_TRF2==1]<- 1 #All-cause cancer
track.FU2$CCT_CANC_TRM[track.FU2$CCT_CANC_TRF2==2]<- 0
track.FU2$CCT_CANC_TRM[track.FU2$CCT_CANC_TRF2==8]<- NA
track.FU2$CCT_CANC_TRM[track.FU2$CCT_CANC_TRF2==9]<- NA

track.FU2$CCT_BCKP_TRM<-NA #Back problems but not fibromyalgia or arthritis (Question not included)

track.FU2$CCT_KIDN_TRM[track.FU2$CCT_KIDN_TRF2==1]<- 1 #Kidney disease
track.FU2$CCT_KIDN_TRM[track.FU2$CCT_KIDN_TRF2==2]<- 0
track.FU2$CCT_KIDN_TRM[track.FU2$CCT_KIDN_TRF2==8]<- NA
track.FU2$CCT_KIDN_TRM[track.FU2$CCT_KIDN_TRF2==9]<- NA

track.FU2$CCT_OTCCT_TRM <- NA #Other long term mental or physical condition (Question not included)

track.FU2$CCT_OAHAND_TRM[track.FU2$CCT_OAHAND_TRF2==1]<- 1 #Hand arthritis
track.FU2$CCT_OAHAND_TRM[track.FU2$CCT_OAHAND_TRF2==2]<- 0
track.FU2$CCT_OAHAND_TRM[track.FU2$CCT_OAHAND_TRF2==8]<- NA
track.FU2$CCT_OAHAND_TRM[track.FU2$CCT_OAHAND_TRF2==9]<- NA

track.FU2$CCT_OAHIP_TRM[track.FU2$CCT_OAHIP_TRF2==1]<- 1 #Hip arthritis
track.FU2$CCT_OAHIP_TRM[track.FU2$CCT_OAHIP_TRF2==2]<- 0
track.FU2$CCT_OAHIP_TRM[track.FU2$CCT_OAHIP_TRF2==8]<- NA
track.FU2$CCT_OAHIP_TRM[track.FU2$CCT_OAHIP_TRF2==9]<- NA

track.FU2$CCT_OAKNEE_TRM[track.FU2$CCT_OAKNEE_TRF2==1]<- 1 #Knee arthritis
track.FU2$CCT_OAKNEE_TRM[track.FU2$CCT_OAKNEE_TRF2==2]<- 0
track.FU2$CCT_OAKNEE_TRM[track.FU2$CCT_OAKNEE_TRF2==8]<- NA
track.FU2$CCT_OAKNEE_TRM[track.FU2$CCT_OAKNEE_TRF2==9]<- NA

track.FU2$CCT_RA_TRM[track.FU2$CCT_RA_TRF2==1]<- 1 #Rheumatoid arthritis
track.FU2$CCT_RA_TRM[track.FU2$CCT_RA_TRF2==2]<- 0
track.FU2$CCT_RA_TRM[track.FU2$CCT_RA_TRF2==8]<- NA
track.FU2$CCT_RA_TRM[track.FU2$CCT_RA_TRF2==9]<- NA

track.FU2$CCT_ARTOT_TRM <- NA #Other arthritis (not included in the data)

track.FU2$DIA_DIAB_TRM[track.FU2$CCT_DIAB_TRF2==1]<- 1 #Diabetes
track.FU2$DIA_DIAB_TRM[track.FU2$CCT_DIAB_TRF2==2]<- 0
track.FU2$DIA_DIAB_TRM[track.FU2$CCT_DIAB_TRF2==8]<- NA
track.FU2$DIA_DIAB_TRM[track.FU2$CCT_DIAB_TRF2==9]<- NA

track.FU2$CCT_HBP_TRM[track.FU2$CCT_HBP_TRF2==1]<- 1 #High blood pressure
track.FU2$CCT_HBP_TRM[track.FU2$CCT_HBP_TRF2==2]<- 0
track.FU2$CCT_HBP_TRM[track.FU2$CCT_HBP_TRF2==8]<- NA
track.FU2$CCT_HBP_TRM[track.FU2$CCT_HBP_TRF2==9]<- NA

track.FU2$CCT_UTHYR_TRM[track.FU2$CCT_UTHYR_TRF2==1]<- 1 #Under active thyroid
track.FU2$CCT_UTHYR_TRM[track.FU2$CCT_UTHYR_TRF2==2]<- 0
track.FU2$CCT_UTHYR_TRM[track.FU2$CCT_UTHYR_TRF2==8]<- NA
track.FU2$CCT_UTHYR_TRM[track.FU2$CCT_UTHYR_TRF2==9]<- NA

track.FU2$CCT_ANGI_TRM[track.FU2$CCT_ANGI_TRF2==1]<- 1 #Angina
track.FU2$CCT_ANGI_TRM[track.FU2$CCT_ANGI_TRF2==2]<- 0
track.FU2$CCT_ANGI_TRM[track.FU2$CCT_ANGI_TRF2==8]<- NA
track.FU2$CCT_ANGI_TRM[track.FU2$CCT_ANGI_TRF2==9]<- NA

track.FU2$CCT_CVA_TRM[track.FU2$CCT_CVA_TRF2==1]<- 1 #Stroke or CVA
track.FU2$CCT_CVA_TRM[track.FU2$CCT_CVA_TRF2==2]<- 0
track.FU2$CCT_CVA_TRM[track.FU2$CCT_CVA_TRF2==8]<- NA
track.FU2$CCT_CVA_TRM[track.FU2$CCT_CVA_TRF2==9]<- NA

track.FU2$CCT_AMI_TRM[track.FU2$CCT_AMI_TRF2==1]<- 1 #myocardial infarction
track.FU2$CCT_AMI_TRM[track.FU2$CCT_AMI_TRF2==2]<- 0
track.FU2$CCT_AMI_TRM[track.FU2$CCT_AMI_TRF2==8]<- NA
track.FU2$CCT_AMI_TRM[track.FU2$CCT_AMI_TRF2==9]<- NA

track.FU2$CCT_OTHYR_TRM[track.FU2$CCT_OTHYR_TRF2==1]<- 1 #Overactive thyroid
track.FU2$CCT_OTHYR_TRM[track.FU2$CCT_OTHYR_TRF2==2]<- 0
track.FU2$CCT_OTHYR_TRM[track.FU2$CCT_OTHYR_TRF2==8]<- NA
track.FU2$CCT_OTHYR_TRM[track.FU2$CCT_OTHYR_TRF2==9]<- NA

track.FU2$CCT_TIA_TRM[track.FU2$CCT_TIA_TRF2==1]<- 1 #Transient Ischemic Attack
track.FU2$CCT_TIA_TRM[track.FU2$CCT_TIA_TRF2==2]<- 0
track.FU2$CCT_TIA_TRM[track.FU2$CCT_TIA_TRF2==8]<- NA
track.FU2$CCT_TIA_TRM[track.FU2$CCT_TIA_TRF2==9]<- NA

track.FU2$CCT_ASTHM_TRM[track.FU2$CCT_ASTHM_TRF2==1]<- 1 #Asthma
track.FU2$CCT_ASTHM_TRM[track.FU2$CCT_ASTHM_TRF2==2]<- 0
track.FU2$CCT_ASTHM_TRM[track.FU2$CCT_ASTHM_TRF2==8]<- NA
track.FU2$CCT_ASTHM_TRM[track.FU2$CCT_ASTHM_TRF2==9]<- NA

track.FU2$CCT_OSTPO_TRM[track.FU2$CCT_OSTPO_TRF2==1]<- 1 #Osteoperosis
track.FU2$CCT_OSTPO_TRM[track.FU2$CCT_OSTPO_TRF2==2]<- 0
track.FU2$CCT_OSTPO_TRM[track.FU2$CCT_OSTPO_TRF2==8]<- NA
track.FU2$CCT_OSTPO_TRM[track.FU2$CCT_OSTPO_TRF2==9]<- NA

track.FU2$CCT_PARK_TRM[track.FU2$PKD_PARK_TRF2==1]<- 1 #Parkinsons
track.FU2$CCT_PARK_TRM[track.FU2$PKD_PARK_TRF2==2]<- 0
track.FU2$CCT_PARK_TRM[track.FU2$PKD_PARK_TRF2==8]<- NA
track.FU2$CCT_PARK_TRM[track.FU2$PKD_PARK_TRF2==9]<- NA

track.FU2$CCT_COPD_TRM[track.FU2$CCT_COPD_TRF2==1]<- 1 #COPD
track.FU2$CCT_COPD_TRM[track.FU2$CCT_COPD_TRF2==2]<- 0
track.FU2$CCT_COPD_TRM[track.FU2$CCT_COPD_TRF2==8]<- NA
track.FU2$CCT_COPD_TRM[track.FU2$CCT_COPD_TRF2==9]<- NA

track.FU2$Chronic_conditions<-track.FU2$CCT_HEART_TRM + track.FU2$CCT_PVD_TRM + track.FU2$CCT_MEMPB_TRM + track.FU2$CCT_ALZH_TRM + track.FU2$CCT_MS_TRM + 
  track.FU2$CCT_EPIL_TRM + track.FU2$CCT_MGRN_TRM + track.FU2$CCT_ULCR_TRM +
  track.FU2$CCT_IBDIBS_TRM + track.FU2$CCT_BOWINC_TRM + track.FU2$CCT_URIINC_TRM + track.FU2$CCT_MACDEG_TRM + track.FU2$CCT_CANC_TRM + track.FU2$CCT_KIDN_TRM + 
  track.FU2$CCT_OAHAND_TRM + track.FU2$CCT_OAHIP_TRM + track.FU2$CCT_OAKNEE_TRM + track.FU2$CCT_RA_TRM  +
  track.FU2$DIA_DIAB_TRM + track.FU2$CCT_HBP_TRM + track.FU2$CCT_UTHYR_TRM + track.FU2$CCT_ANGI_TRM + track.FU2$CCT_CVA_TRM + track.FU2$CCT_AMI_TRM + track.FU2$CCT_OTHYR_TRM + 
  track.FU2$CCT_TIA_TRM + track.FU2$CCT_ASTHM_TRM + track.FU2$CCT_OSTPO_TRM + track.FU2$CCT_PARK_TRM + track.FU2$CCT_COPD_TRM

#Restless Sleep (≥ 3-4 days/week)
track.FU2$RSTLS_Sleep<-NA
track.FU2$RSTLS_Sleep[track.FU2$DEP_RSTLS_TRF2<3]<-1
track.FU2$RSTLS_Sleep[track.FU2$DEP_RSTLS_TRF2>=3 & track.FU2$DEP_RSTLS_TRF2<8]<-0
track.FU2$RSTLS_Sleep[track.FU2$DEP_RSTLS_TRF2>4]<-NA

#Finalize data set

track.FU2.1<-track.FU2[c(1:3,78:86,108,111,122:128,88,162)]

names(track.FU2.1) <-paste(names(track.FU2.1),"_2", sep="")

track.FU2.Final<- rename(track.FU2.1, "ID" = "entity_id_2")

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#FU2 (Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_FUP2_COG")

trackFU2cogs<-read.csv("23SP001_McMaster_PRaina_Cognition_Tra_FUP2_v0.csv")#Comprehensive Cohort Baseline

track.FU2cogs<-trackFU2cogs[c(1,64:66,110,109,41,42,48,47)]

############Cognitive Function##############

#~~~~~~Animal Fluency~~~~~~~~~~~#
track.FU2cogs$Animal_Fluency_Strict<-track.FU2cogs$COG_AFT_SCORE_1_TRF2
track.FU2cogs$Animal_Fluency_Lenient<-track.FU2cogs$COG_AFT_SCORE_2_TRF2
track.FU2cogs$Animal_Fluency_Lenient[track.FU2cogs$Animal_Fluency_Lenient<0]<-NA
track.FU2cogs$Animal_Fluency_Strict[track.FU2cogs$Animal_Fluency_Strict<0]<-NA

track.FU2cogs$Animal_Fluency_Lang<-NA
track.FU2cogs$Animal_Fluency_Lang[track.FU2cogs$COG_AFT_STARTLANG_TRF2=="en"]<-"English"
track.FU2cogs$Animal_Fluency_Lang[track.FU2cogs$COG_AFT_STARTLANG_TRF2=="fr"]<-"French"

#~~~~~~~~Mental Alteration Test~~~~~~~~~~#
track.FU2cogs$MAT_Lang<-NA
track.FU2cogs$MAT_Lang[track.FU2cogs$COG_MAT_STARTLANG_TRF2=="en"]<-"English"
track.FU2cogs$MAT_Lang[track.FU2cogs$COG_MAT_STARTLANG_TRF2=="fr"]<-"French"

track.FU2cogs$MAT_Score<-track.FU2cogs$COG_MAT_SCORE_TRF2

track.FU2cogs$MAT_Score[track.FU2cogs$MAT_Score<0]<-NA


#~~~~~~~~RVLT~~~~~~~~~~~~~~~~#
#Rey-Immediate Recall
track.FU2cogs$RVLT_Immediate_Lang<- NA
track.FU2cogs$RVLT_Immediate_Lang[track.FU2cogs$COG_REYI_STARTLANG_TRF2=="en"]<-"English"
track.FU2cogs$RVLT_Immediate_Lang[track.FU2cogs$COG_REYI_STARTLANG_TRF2=="fr"]<-"French"

track.FU2cogs$RVLT_Immediate_Score<-track.FU2cogs$COG_REYI_SCORE_TRF2

track.FU2cogs$RVLT_Immediate_Score[track.FU2cogs$RVLT_Immediate_Score<0]<-NA

#Rey-Delayed Recall
track.FU2cogs$RVLT_Delayed_Lang<- NA
track.FU2cogs$RVLT_Delayed_Lang[track.FU2cogs$COG_REYII_STARTLANG_TRF2=="en"]<-"English"
track.FU2cogs$RVLT_Delayed_Lang[track.FU2cogs$COG_REYII_STARTLANG_TRF2=="fr"]<-"French"

track.FU2cogs$RVLT_Delayed_Score<-track.FU2cogs$COG_REYII_SCORE_TRF2

track.FU2cogs$RVLT_Delayed_Score[track.FU2cogs$RVLT_Delayed_Score<0]<-NA

track.FU2cogs1 <- track.FU2cogs[c(1,11:19)]

names(track.FU2cogs1) <-paste(names(track.FU2cogs1),"_2", sep="")

track.FU2cogs.Final<- rename(track.FU2cogs1, "ID" = "entity_id_2")

1.3) Data merging

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#Combine Datasets
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

#Combine Baseline dataframes
Track.Baseline.Final <- merge(track.BL.Final, track.BLcogs.Final, by = "ID")

#Combine FU1 dataframes
Track.FU1.Final <- merge(track.FU1.Final, track.FU1cogs.Final, by = "ID")

#Combine FU2 dataframes
Track.FU2.Final <- merge(track.FU2.Final, track.FU2cogs.Final, by = "ID")

#Combine data across different time points into full data set
Track.BL.FU1 <- merge(Track.Baseline.Final, Track.FU1.Final, by = "ID")

Track.Full <- merge(Track.BL.FU1, Track.FU2.Final, by = "ID")

2) Final data-set development

This section details how our final data-set was developed. Our final data set included cognitively healthy participants at baseline, FU1, and FU2 with complete baseline neuropsycholoigcal testing (including education level, which was necessary for standardized scores). Thus our final sample size included in our linear mixed models was N=11,355.

Track.Final_Data <- subset(Track.Full, SCI_0=="No" & Dementia_0=="No" & SCI_1=="No" & Dementia_1 =="No" & 
                       SCI_2=="No" & Dementia_2 == "No" & !is.na(Animal_Fluency_Strict_0) & !is.na(MAT_Score_0) & !is.na(Education4_0) &
                       !is.na(RVLT_Immediate_Score_0) & !is.na(RVLT_Delayed_Score_0))

We then normalized all cognitive variables for language, age, and biological sex. Score standardization is dependent upon the following steps:

1. Create a predicted score based on age, sex, and test language
2. Create residualized score (i.e., obtained - predicted)
3. Develop a standardized residual score adjusted for age, sex, and education
4. Develop standardized z-score adjusted for age, sex, and education
5. Develop standardized score using z-score with a mean of 10 and a SD of 3.

Tracking.Adjusted_Full <- Track.Final_Data %>%
  mutate(
    #Baseline Scores
    
    #RVLT Immediate Score
    RVLT_Immediate_Predicted_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 7.768 - 0.050*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 7.449 - 0.036*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ 10.095 - 0.073*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ 9.686 - 0.064*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 8.077 - 0.043*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 9.806 - 0.059*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ 9.161 - 0.047*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ 9.804 - 0.053*Age_0,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 5.666 - 0.025*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 8.953 - 0.067*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ 7.662 - 0.039*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ 8.829 - 0.057*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 6.976 - 0.031*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 8.667 - 0.045*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ 9.502 - 0.061*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ 9.013 - 0.047*Age_0
    ),
    RVLT_Immediate_Residual_0 = RVLT_Immediate_Score_0 - RVLT_Immediate_Predicted_0,
    RVLT_Immediate_Z_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.471,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.525,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.611,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.675,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.528,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.643,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.694,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.802,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.290,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.473,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.913,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.641,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.623,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.595,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.605,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.715
    ),
    RVLT_Immediate_Normed_0 = RVLT_Immediate_Z_0*3 +10,
    RVLT_Immediate_Normed_0 = if_else(RVLT_Immediate_Normed_0 < 0, 0.01, RVLT_Immediate_Normed_0),
    
    #RVLT Delayed Score
    RVLT_Delayed_Predicted_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 6.628 - 0.062*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 6.851 - 0.058*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ 8.289 - 0.076*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ 8.165 - 0.070*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 7.163 - 0.055*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 8.115 - 0.063*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ 8.151 - 0.059*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ 8.844 - 0.066*Age_0,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 4.802 - 0.036*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 8.219 - 0.083*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ 9.721 - 0.097*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ 7.048 - 0.055*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 6.280 - 0.044*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 6.999 - 0.042*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ 9.081 - 0.074*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ 8.712 - 0.066*Age_0
    ),
    RVLT_Delayed_Residual_0 = RVLT_Delayed_Score_0 - RVLT_Delayed_Predicted_0,
    RVLT_Delayed_Z_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.534,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.739,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.802,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.890,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.787,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/2.005,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.869,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/2.135,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.559,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.571,
      Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.815,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.721,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.859,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.793,
      Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.901,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.890
    ),
    RVLT_Delayed_Normed_0 = RVLT_Delayed_Z_0*3 +10,
    RVLT_Delayed_Normed_0 = if_else(RVLT_Delayed_Normed_0 < 0, 0.01, RVLT_Delayed_Normed_0),
    
    #Animal Fluency
    Animal_Fluency_Predicted_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 23.132 - 0.095*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 28.923 - 0.157*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ 32.513 - 0.202*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ 31.143 - 0.168*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 23.433 - 0.114*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 29.912 - 0.181*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ 30.764 - 0.178*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ 32.003 - 0.186*Age_0,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 26.034 - 0.152*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 33.358 - 0.241*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ 36.511 - 0.277*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ 30.193 - 0.179*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 21.460 - 0.089*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 21.355 - 0.070*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ 30.881 - 0.205*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ 29.961 - 0.180*Age_0
    ),
    Animal_Fluency_Residual_0 = Animal_Fluency_Strict_0 - Animal_Fluency_Predicted_0,
    Animal_Fluency_Z_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.145,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.348,
      Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.163,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.354,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/4.665,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/4.728,
      Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.176,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.369,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/3.911,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.889,
      Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/5.061,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.869,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.178,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.321,
      Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.468,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.940
    ),
    Animal_Fluency_Normed_0 = Animal_Fluency_Z_0*3 +10,
    Animal_Fluency_Normed_0 = if_else(Animal_Fluency_Normed_0 < 0, 0.01, Animal_Fluency_Normed_0),
    
    #Mental Alteration Test    
    MAT_Predicted_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ 33.295 - 0.161*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ 34.074 - 0.123*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ 41.488 - 0.219*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ 40.573 - 0.190*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ 39.102 - 0.251*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ 41.657 - 0.246*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ 36.877 - 0.168*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ 38.849 - 0.188*Age_0,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ 36.630 - 0.252*Age_0,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ 38.784 - 0.181*Age_0,
      Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ 51.105 - 0.381*Age_0,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ 44.106 - 0.257*Age_0,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ 34.814 - 0.214*Age_0,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ 38.756 - 0.202*Age_0,
      Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ 47.024 - 0.315*Age_0,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ 41.717 - 0.234*Age_0
    ),
    MAT_Residual_0 = MAT_Score_0 - MAT_Predicted_0,
    MAT_Z_0 = case_when(
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.602,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.702,
      Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.490,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.727,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.080,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.139,
      Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ MAT_Residual_0/6.915,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ MAT_Residual_0/6.979,
      Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.589,
      Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.234,
      Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.314,
      Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.609,
      Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.803,
      Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.079,
      Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.451,
      Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.734
    ),
    MAT_Normed_0 = MAT_Z_0*3 +10,
    MAT_Normed_0 = if_else(MAT_Normed_0 < 0, 0.01, MAT_Normed_0),
    
    #Global Cognition Composite Score
    Global_Composite_0 = RVLT_Immediate_Z_0 + RVLT_Delayed_Z_0 + Animal_Fluency_Z_0 + MAT_Z_0,
    
    #FU1 Scores
    
    #RVLT Immediate Score
    RVLT_Immediate_Predicted_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 7.768 - 0.050*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 7.449 - 0.036*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ 10.095 - 0.073*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ 9.686 - 0.064*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 8.077 - 0.043*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 9.806 - 0.059*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ 9.161 - 0.047*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ 9.804 - 0.053*Age_1,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 5.666 - 0.025*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 8.953 - 0.067*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ 7.662 - 0.039*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ 8.829 - 0.057*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 6.976 - 0.031*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 8.667 - 0.045*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ 9.502 - 0.061*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ 9.013 - 0.047*Age_1
    ),
    RVLT_Immediate_Residual_1 = RVLT_Immediate_Score_1 - RVLT_Immediate_Predicted_1,
    RVLT_Immediate_Z_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.471,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.525,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.611,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.675,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.528,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.643,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.694,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.802,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.290,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.473,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.913,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.641,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.623,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.595,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.605,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.715
    ),
    RVLT_Immediate_Normed_1 = RVLT_Immediate_Z_1*3 + 10,
    RVLT_Immediate_Normed_1 = if_else(RVLT_Immediate_Normed_1 < 0, 0.01, RVLT_Immediate_Normed_1),
    
    
    #RVLT Delayed Score
    RVLT_Delayed_Predicted_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 6.628 - 0.062*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 6.851 - 0.058*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ 8.289 - 0.076*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ 8.165 - 0.070*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 7.163 - 0.055*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 8.115 - 0.063*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ 8.151 - 0.059*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ 8.844 - 0.066*Age_1,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 4.802 - 0.036*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 8.219 - 0.083*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ 9.721 - 0.097*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ 7.048 - 0.055*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 6.280 - 0.044*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 6.999 - 0.042*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ 9.081 - 0.074*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ 8.712 - 0.066*Age_1
    ),
    RVLT_Delayed_Residual_1 = RVLT_Delayed_Score_1 - RVLT_Delayed_Predicted_1,
    RVLT_Delayed_Z_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.534,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.739,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.802,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.890,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.787,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/2.005,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.869,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/2.135,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.559,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.571,
      Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.815,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.721,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.859,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.793,
      Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.901,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.890
    ),
    RVLT_Delayed_Normed_1 = RVLT_Delayed_Z_1*3 + 10,
    RVLT_Delayed_Normed_1 = if_else(RVLT_Delayed_Normed_1 < 0, 0.01, RVLT_Delayed_Normed_1),
    
    #Animal Fluency
    Animal_Fluency_Predicted_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 23.132 - 0.095*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 28.923 - 0.157*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ 32.513 - 0.202*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ 31.143 - 0.168*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 23.433 - 0.114*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 29.912 - 0.181*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ 30.764 - 0.178*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ 32.003 - 0.186*Age_1,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 26.034 - 0.152*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 33.358 - 0.241*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ 36.511 - 0.277*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ 30.193 - 0.179*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 21.460 - 0.089*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 21.355 - 0.070*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ 30.881 - 0.205*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ 29.961 - 0.180*Age_1
    ),
    Animal_Fluency_Residual_1 = Animal_Fluency_Strict_1 - Animal_Fluency_Predicted_1,
    Animal_Fluency_Z_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.145,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.348,
      Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.163,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.354,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/4.665,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/4.728,
      Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.176,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.369,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/3.911,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.889,
      Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/5.061,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.869,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.178,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.321,
      Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.468,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.940
    ),
    Animal_Fluency_Normed_1 = Animal_Fluency_Z_1*3 + 10,
    Animal_Fluency_Normed_1 = if_else(Animal_Fluency_Normed_1 < 0, 0.01, Animal_Fluency_Normed_1),
    
    #Mental Alteration Test
    MAT_Predicted_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ 33.295 - 0.161*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ 34.074 - 0.123*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ 41.488 - 0.219*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ 40.573 - 0.190*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ 39.102 - 0.251*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ 41.657 - 0.246*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ 36.877 - 0.168*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ 38.849 - 0.188*Age_1,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ 36.630 - 0.252*Age_1,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ 38.784 - 0.181*Age_1,
      Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ 51.105 - 0.381*Age_1,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ 44.106 - 0.257*Age_1,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ 34.814 - 0.214*Age_1,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ 38.756 - 0.202*Age_1,
      Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ 47.024 - 0.315*Age_1,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ 41.717 - 0.234*Age_1
    ),
    MAT_Residual_1 = MAT_Score_1 - MAT_Predicted_1,
    MAT_Z_1 = case_when(
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.602,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.702,
      Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.490,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.727,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.080,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.139,
      Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ MAT_Residual_1/6.915,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ MAT_Residual_1/6.979,
      Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ MAT_Residual_1/7.589,
      Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ MAT_Residual_1/7.234,
      Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ MAT_Residual_1/6.314,
      Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ MAT_Residual_1/7.609,
      Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ MAT_Residual_1/6.803,
      Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ MAT_Predicted_1/7.079,
      Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ MAT_Predicted_1/6.451,
      Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ MAT_Predicted_1/6.734
    ),
    MAT_Normed_1 = MAT_Z_1*3 + 10,
    MAT_Normed_1 = if_else(MAT_Normed_1 < 0, 0.01, MAT_Normed_1),
    
    #Global Cognition Composite Score
    Global_Composite_1 = RVLT_Immediate_Z_1 + RVLT_Delayed_Z_1 + Animal_Fluency_Z_1 + MAT_Z_1,
    
    
    #FU2 Scores
    
    #RVLT Immediate Score
    RVLT_Immediate_Predicted_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 7.768 - 0.050*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 7.449 - 0.036*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ 10.095 - 0.073*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ 9.686 - 0.064*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 8.077 - 0.043*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 9.806 - 0.059*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ 9.161 - 0.047*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ 9.804 - 0.053*Age_2,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 5.666 - 0.025*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 8.953 - 0.067*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ 7.662 - 0.039*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ 8.829 - 0.057*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 6.976 - 0.031*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 8.667 - 0.045*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ 9.502 - 0.061*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ 9.013 - 0.047*Age_2
    ),
    RVLT_Immediate_Residual_2 = RVLT_Immediate_Score_2 - RVLT_Immediate_Predicted_2,
    RVLT_Immediate_Z_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.471,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.525,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.611,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.675,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.528,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.643,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.694,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.802,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.290,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.473,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.913,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.641,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.623,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.595,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.605,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.715
    ),
    RVLT_Immediate_Normed_2 = RVLT_Immediate_Z_2*3 + 10,
    RVLT_Immediate_Normed_2 = if_else(RVLT_Immediate_Normed_2 < 0, 0.01, RVLT_Immediate_Normed_2),
    
    #RVLT Delayed Score
    RVLT_Delayed_Predicted_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 6.628 - 0.062*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 6.851 - 0.058*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ 8.289 - 0.076*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ 8.165 - 0.070*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 7.163 - 0.055*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 8.115 - 0.063*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ 8.151 - 0.059*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ 8.844 - 0.066*Age_2,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 4.802 - 0.036*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 8.219 - 0.083*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ 9.721 - 0.097*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ 7.048 - 0.055*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 6.280 - 0.044*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 6.999 - 0.042*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ 9.081 - 0.074*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ 8.712 - 0.066*Age_2
    ),
    RVLT_Delayed_Residual_2 = RVLT_Delayed_Score_2 - RVLT_Delayed_Predicted_2,
    RVLT_Delayed_Z_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.534,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.739,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.802,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.890,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.787,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/2.005,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.869,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/2.135,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.559,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.571,
      Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.815,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.721,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.859,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.793,
      Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.901,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.890
    ),
    RVLT_Delayed_Normed_2 = RVLT_Delayed_Z_2*3 + 10,
    RVLT_Delayed_Normed_2 = if_else(RVLT_Delayed_Normed_2 < 0, 0.01, RVLT_Delayed_Normed_2),
    
    #Animal Fluency
    Animal_Fluency_Predicted_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 23.132 - 0.095*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 28.923 - 0.157*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ 32.513 - 0.202*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ 31.143 - 0.168*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 23.433 - 0.114*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 29.912 - 0.181*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ 30.764 - 0.178*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ 32.003 - 0.186*Age_2,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 26.034 - 0.152*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 33.358 - 0.241*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ 36.511 - 0.277*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ 30.193 - 0.179*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 21.460 - 0.089*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 21.355 - 0.070*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ 30.881 - 0.205*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ 29.961 - 0.180*Age_2
    ),
    Animal_Fluency_Residual_2 = Animal_Fluency_Strict_2 - Animal_Fluency_Predicted_2,
    Animal_Fluency_Z_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.145,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.348,
      Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.163,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.354,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/4.665,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/4.728,
      Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.176,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.369,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/3.911,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.889,
      Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/5.061,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.869,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.178,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.321,
      Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.468,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.940
    ),
    Animal_Fluency_Normed_2 = Animal_Fluency_Z_2*3 + 10,
    Animal_Fluency_Normed_2 = if_else(Animal_Fluency_Normed_2 < 0, 0.01, Animal_Fluency_Normed_2),
    
    #Mental Alteration Test
    MAT_Predicted_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ 33.295 - 0.161*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ 34.074 - 0.123*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ 41.488 - 0.219*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ 40.573 - 0.190*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ 39.102 - 0.251*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ 41.657 - 0.246*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ 36.877 - 0.168*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ 38.849 - 0.188*Age_2,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ 36.630 - 0.252*Age_2,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ 38.784 - 0.181*Age_2,
      Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ 51.105 - 0.381*Age_2,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ 44.106 - 0.257*Age_2,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ 34.814 - 0.214*Age_2,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ 38.756 - 0.202*Age_2,
      Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ 47.024 - 0.315*Age_2,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ 41.717 - 0.234*Age_2
    ),
    MAT_Residual_2 = MAT_Score_2 - MAT_Predicted_2,
    MAT_Z_2 = case_when(
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.602,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.702,
      Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.490,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.727,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.080,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.139,
      Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ MAT_Residual_2/6.915,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ MAT_Residual_2/6.979,
      Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.589,
      Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.234,
      Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.314,
      Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.609,
      Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.803,
      Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.079,
      Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.451,
      Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.734
    ),
    MAT_Normed_2 = MAT_Z_2*3 + 10,
    MAT_Normed_2 = if_else(MAT_Normed_2 < 0, 0.01, MAT_Normed_2),
    
    #Global Cognition Composite Score
    Global_Composite_2 = RVLT_Immediate_Z_2 + RVLT_Delayed_Z_2 + Animal_Fluency_Z_0 + MAT_Z_2
  )

Finally, we categorized participants as having their follow-up 2 data collected before or after the start of the COVID-19 pandemic.

Tracking.Adjusted_Final <- Tracking.Adjusted_Full %>%
  mutate(timestamp = ymd_hms(startdate_TRF2_2, tz = "EST"))

## Date in ISO8601 format; converting timezone from UTC to "EST".

start_time <- as.POSIXct("2020-03-11 00:00:00", tz = "EST")
Tracking.Adjusted_Final$Pandemic<-NA
Tracking.Adjusted_Final$Pandemic[Tracking.Adjusted_Final$timestamp>=start_time]<-"FU2 data collected after COVID-19"
Tracking.Adjusted_Final$Pandemic[Tracking.Adjusted_Final$timestamp<start_time]<-"FU2 data collected before COVID-19"

2.1) Flow-chart for participants

Full tracking cohort at baseline (N=21,241)

Track.Baseline.total <- Track.Baseline.Final %>%
  count()
print(Track.Baseline.total)

##       n
## 1 21241

Excluding tracking cohort participants lost to follow-up (N=14,697)

Track.Complete <- Track.Full %>%
  count()
print(Track.Complete)

##       n
## 1 14697

Baseline tracking cohort removing individuals who reported SCI or dementia at baseline, FU1, or FU2 (N=13,934)

Track.SCI_Dementia <- Track.Full %>%
  group_by(SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2) %>%
  count()
print(Track.SCI_Dementia)

## # A tibble: 40 × 7
## # Groups:   SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2 [40]
##    SCI_0 Dementia_0 SCI_1 Dementia_1 SCI_2 Dementia_2     n
##    <chr> <chr>      <chr> <chr>      <chr> <chr>      <int>
##  1 No    No         No    No         No    No         13934
##  2 No    No         No    No         No    Yes            7
##  3 No    No         No    No         No    <NA>           7
##  4 No    No         No    No         Yes   No           143
##  5 No    No         No    No         Yes   Yes           18
##  6 No    No         No    No         Yes   <NA>           2
##  7 No    No         No    No         <NA>  No            15
##  8 No    No         No    No         <NA>  Yes            1
##  9 No    No         No    No         <NA>  <NA>         160
## 10 No    No         No    Yes        No    <NA>           3
## # ℹ 30 more rows

Baseline tracking cohort removing individuals with dementia or SCI and without full cog data (N=11,355)

Track.FullCogs <- Track.Full %>%
  group_by(SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2, 
           !is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0), !is.na(RVLT_Delayed_Score_0),!is.na(Animal_Fluency_Strict_0)) %>%
  count()
print(Track.FullCogs)

## # A tibble: 105 × 11
## # Groups:   SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2,
## #   !is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0),
## #   !is.na(RVLT_Delayed_Score_0), !is.na(Animal_Fluency_Strict_0) [105]
##    SCI_0 Dementia_0 SCI_1 Dementia_1 SCI_2 Dementia_2 `!is.na(MAT_Score_0)`
##    <chr> <chr>      <chr> <chr>      <chr> <chr>      <lgl>                
##  1 No    No         No    No         No    No         FALSE                
##  2 No    No         No    No         No    No         FALSE                
##  3 No    No         No    No         No    No         FALSE                
##  4 No    No         No    No         No    No         FALSE                
##  5 No    No         No    No         No    No         FALSE                
##  6 No    No         No    No         No    No         FALSE                
##  7 No    No         No    No         No    No         FALSE                
##  8 No    No         No    No         No    No         FALSE                
##  9 No    No         No    No         No    No         TRUE                 
## 10 No    No         No    No         No    No         TRUE                 
## # ℹ 95 more rows
## # ℹ 4 more variables: `!is.na(RVLT_Immediate_Score_0)` <lgl>,
## #   `!is.na(RVLT_Delayed_Score_0)` <lgl>,
## #   `!is.na(Animal_Fluency_Strict_0)` <lgl>, n <int>

Final sample size (N=11,355) with complete cognitive data at FU1 grouped by Pandemic cohort

Track.FullCogs2 <- Tracking.Adjusted_Final %>%
  group_by(!is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0), !is.na(RVLT_Delayed_Score_0),!is.na(Animal_Fluency_Strict_0),
           !is.na(MAT_Score_1), !is.na(RVLT_Immediate_Score_1), !is.na(RVLT_Delayed_Score_1),!is.na(Animal_Fluency_Strict_1), Pandemic) %>%
  count()
print(Track.FullCogs2)

## # A tibble: 29 × 10
## # Groups:   !is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0),
## #   !is.na(RVLT_Delayed_Score_0), !is.na(Animal_Fluency_Strict_0),
## #   !is.na(MAT_Score_1), !is.na(RVLT_Immediate_Score_1),
## #   !is.na(RVLT_Delayed_Score_1), !is.na(Animal_Fluency_Strict_1), Pandemic
## #   [29]
##    `!is.na(MAT_Score_0)` `!is.na(RVLT_Immediate_Score_0)` !is.na(RVLT_Delayed_…¹
##    <lgl>                 <lgl>                            <lgl>                 
##  1 TRUE                  TRUE                             TRUE                  
##  2 TRUE                  TRUE                             TRUE                  
##  3 TRUE                  TRUE                             TRUE                  
##  4 TRUE                  TRUE                             TRUE                  
##  5 TRUE                  TRUE                             TRUE                  
##  6 TRUE                  TRUE                             TRUE                  
##  7 TRUE                  TRUE                             TRUE                  
##  8 TRUE                  TRUE                             TRUE                  
##  9 TRUE                  TRUE                             TRUE                  
## 10 TRUE                  TRUE                             TRUE                  
## # ℹ 19 more rows
## # ℹ abbreviated name: ¹​`!is.na(RVLT_Delayed_Score_0)`
## # ℹ 7 more variables: `!is.na(Animal_Fluency_Strict_0)` <lgl>,
## #   `!is.na(MAT_Score_1)` <lgl>, `!is.na(RVLT_Immediate_Score_1)` <lgl>,
## #   `!is.na(RVLT_Delayed_Score_1)` <lgl>,
## #   `!is.na(Animal_Fluency_Strict_1)` <lgl>, Pandemic <chr>, n <int>

Final sample size (N=11,355) with complete cognitive data at FU2 grouped by Pandemic cohort

Track.FullCogs3 <- Tracking.Adjusted_Final %>%
  group_by(!is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0), !is.na(RVLT_Delayed_Score_0),!is.na(Animal_Fluency_Strict_0),
           !is.na(MAT_Score_2), !is.na(RVLT_Immediate_Score_2), !is.na(RVLT_Delayed_Score_2),!is.na(Animal_Fluency_Strict_2), Pandemic) %>%
  count()
print(Track.FullCogs3)

## # A tibble: 25 × 10
## # Groups:   !is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0),
## #   !is.na(RVLT_Delayed_Score_0), !is.na(Animal_Fluency_Strict_0),
## #   !is.na(MAT_Score_2), !is.na(RVLT_Immediate_Score_2),
## #   !is.na(RVLT_Delayed_Score_2), !is.na(Animal_Fluency_Strict_2), Pandemic
## #   [25]
##    `!is.na(MAT_Score_0)` `!is.na(RVLT_Immediate_Score_0)` !is.na(RVLT_Delayed_…¹
##    <lgl>                 <lgl>                            <lgl>                 
##  1 TRUE                  TRUE                             TRUE                  
##  2 TRUE                  TRUE                             TRUE                  
##  3 TRUE                  TRUE                             TRUE                  
##  4 TRUE                  TRUE                             TRUE                  
##  5 TRUE                  TRUE                             TRUE                  
##  6 TRUE                  TRUE                             TRUE                  
##  7 TRUE                  TRUE                             TRUE                  
##  8 TRUE                  TRUE                             TRUE                  
##  9 TRUE                  TRUE                             TRUE                  
## 10 TRUE                  TRUE                             TRUE                  
## # ℹ 15 more rows
## # ℹ abbreviated name: ¹​`!is.na(RVLT_Delayed_Score_0)`
## # ℹ 7 more variables: `!is.na(Animal_Fluency_Strict_0)` <lgl>,
## #   `!is.na(MAT_Score_2)` <lgl>, `!is.na(RVLT_Immediate_Score_2)` <lgl>,
## #   `!is.na(RVLT_Delayed_Score_2)` <lgl>,
## #   `!is.na(Animal_Fluency_Strict_2)` <lgl>, Pandemic <chr>, n <int>

Final sample size (N=11,355) and number of participants with PASE score at baseline (N=9,181)

```r
PASEBL <- Tracking.Adjusted_Final %>%
  group_by(!is.na(PASE_TOTAL_0), Pandemic) %>%
  count()
print(PASEBL)

## # A tibble: 4 × 3
## # Groups:   !is.na(PASE_TOTAL_0), Pandemic [4]
##   `!is.na(PASE_TOTAL_0)` Pandemic                               n
##   <lgl>                  <chr>                              <int>
## 1 FALSE                  FU2 data collected after COVID-19   1027
## 2 FALSE                  FU2 data collected before COVID-19  1147
## 3 TRUE                   FU2 data collected after COVID-19   4154
## 4 TRUE                   FU2 data collected before COVID-19  5027

Final sample size (N=11,355) and number of participants with PASE score at baseline or FU1 (N=1,945)

PASEBL <- Tracking.Adjusted_Final %>%
  group_by(!is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1),Pandemic) %>%
  count()
print(PASEBL)

## # A tibble: 8 × 4
## # Groups:   !is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1), Pandemic [8]
##   `!is.na(PASE_TOTAL_0)` `!is.na(PASE_TOTAL_1)` Pandemic                       n
##   <lgl>                  <lgl>                  <chr>                      <int>
## 1 FALSE                  FALSE                  FU2 data collected after …   882
## 2 FALSE                  FALSE                  FU2 data collected before…  1016
## 3 FALSE                  TRUE                   FU2 data collected after …   145
## 4 FALSE                  TRUE                   FU2 data collected before…   131
## 5 TRUE                   FALSE                  FU2 data collected after …  3226
## 6 TRUE                   FALSE                  FU2 data collected before…  4010
## 7 TRUE                   TRUE                   FU2 data collected after …   928
## 8 TRUE                   TRUE                   FU2 data collected before…  1017

Final sample size (N=11,355) and number of participants with PASE score at baseline, FU1, or FU2 (N=762)

PASEBL <- Tracking.Adjusted_Final %>%
  group_by(!is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1), , !is.na(PASE_TOTAL_2),Pandemic) %>%
  count()
print(PASEBL)

## # A tibble: 16 × 5
## # Groups:   !is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1), !is.na(PASE_TOTAL_2),
## #   Pandemic [16]
##    `!is.na(PASE_TOTAL_0)` `!is.na(PASE_TOTAL_1)` `!is.na(PASE_TOTAL_2)` Pandemic
##    <lgl>                  <lgl>                  <lgl>                  <chr>   
##  1 FALSE                  FALSE                  FALSE                  FU2 dat…
##  2 FALSE                  FALSE                  FALSE                  FU2 dat…
##  3 FALSE                  FALSE                  TRUE                   FU2 dat…
##  4 FALSE                  FALSE                  TRUE                   FU2 dat…
##  5 FALSE                  TRUE                   FALSE                  FU2 dat…
##  6 FALSE                  TRUE                   FALSE                  FU2 dat…
##  7 FALSE                  TRUE                   TRUE                   FU2 dat…
##  8 FALSE                  TRUE                   TRUE                   FU2 dat…
##  9 TRUE                   FALSE                  FALSE                  FU2 dat…
## 10 TRUE                   FALSE                  FALSE                  FU2 dat…
## 11 TRUE                   FALSE                  TRUE                   FU2 dat…
## 12 TRUE                   FALSE                  TRUE                   FU2 dat…
## 13 TRUE                   TRUE                   FALSE                  FU2 dat…
## 14 TRUE                   TRUE                   FALSE                  FU2 dat…
## 15 TRUE                   TRUE                   TRUE                   FU2 dat…
## 16 TRUE                   TRUE                   TRUE                   FU2 dat…
## # ℹ 1 more variable: n <int>

Final sample size (N=11,355) and number of participants with sleep data at baseline (N=11,334)

PASEBL <- Tracking.Adjusted_Final %>%
  group_by(!is.na(RSTLS_Sleep_0),Pandemic) %>%
  count()
print(PASEBL)

## # A tibble: 4 × 3
## # Groups:   !is.na(RSTLS_Sleep_0), Pandemic [4]
##   `!is.na(RSTLS_Sleep_0)` Pandemic                               n
##   <lgl>                   <chr>                              <int>
## 1 FALSE                   FU2 data collected after COVID-19     11
## 2 FALSE                   FU2 data collected before COVID-19    10
## 3 TRUE                    FU2 data collected after COVID-19   5170
## 4 TRUE                    FU2 data collected before COVID-19  6164

Final sample size (N=11,355) and number of participants with sleep data at baseline or FU1 (N=11,304)

PASEBL <- Tracking.Adjusted_Final %>%
  group_by(!is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1),Pandemic) %>%
  count()
print(PASEBL)

## # A tibble: 6 × 4
## # Groups:   !is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1), Pandemic [6]
##   `!is.na(RSTLS_Sleep_0)` `!is.na(RSTLS_Sleep_1)` Pandemic                     n
##   <lgl>                   <lgl>                   <chr>                    <int>
## 1 FALSE                   TRUE                    FU2 data collected afte…    11
## 2 FALSE                   TRUE                    FU2 data collected befo…    10
## 3 TRUE                    FALSE                   FU2 data collected afte…     7
## 4 TRUE                    FALSE                   FU2 data collected befo…    23
## 5 TRUE                    TRUE                    FU2 data collected afte…  5163
## 6 TRUE                    TRUE                    FU2 data collected befo…  6141

Final sample size (N=11,355) and number of participants with sleep data at baseline, FU1, or FU2 (N=11,270)

PASEBL <- Tracking.Adjusted_Final %>%
  group_by(!is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1), , !is.na(RSTLS_Sleep_2),Pandemic) %>%
  count()
print(PASEBL)

## # A tibble: 8 × 5
## # Groups:   !is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1),
## #   !is.na(RSTLS_Sleep_2), Pandemic [8]
##   `!is.na(RSTLS_Sleep_0)` !is.na(RSTLS_Sleep_1…¹ !is.na(RSTLS_Sleep_2…² Pandemic
##   <lgl>                   <lgl>                  <lgl>                  <chr>   
## 1 FALSE                   TRUE                   TRUE                   FU2 dat…
## 2 FALSE                   TRUE                   TRUE                   FU2 dat…
## 3 TRUE                    FALSE                  TRUE                   FU2 dat…
## 4 TRUE                    FALSE                  TRUE                   FU2 dat…
## 5 TRUE                    TRUE                   FALSE                  FU2 dat…
## 6 TRUE                    TRUE                   FALSE                  FU2 dat…
## 7 TRUE                    TRUE                   TRUE                   FU2 dat…
## 8 TRUE                    TRUE                   TRUE                   FU2 dat…
## # ℹ abbreviated names: ¹​`!is.na(RSTLS_Sleep_1)`, ²​`!is.na(RSTLS_Sleep_2)`
## # ℹ 1 more variable: n <int>

3) Participant characteristics at baseline

Create factor variables for PASE sedentary behaviour and sleep score

BL.data<-Tracking.Adjusted_Final
BL.data$PASE_Q1B_0 <- as.factor(ifelse(BL.data$PASE_Q1B_0==10, 1, 0))
BL.data$RSTLS_Sleep_0 <- as.factor(BL.data$RSTLS_Sleep_0)

Final baseline sample (N= 11,355)

Baseline<-dput(names(BL.data[c(5,4,14,12,6,7,8,9,10,11,15,18,19,21,98,102,106,110,29,31,27,24,13,22,23)]))

## c("Age_0", "Sex_0", "BMI_0", "Ethnicity_0", "Relationship_status_0", 
## "Education4_0", "Income_Level_0", "Living_status_0", "Alcohol_0", 
## "Smoking_Status_0", "CESD_10_0", "Anxiety_0", "Mood_Disord_0", 
## "Chronic_conditions_0", "RVLT_Immediate_Normed_0", "RVLT_Delayed_Normed_0", 
## "Animal_Fluency_Normed_0", "MAT_Normed_0", "RVLT_Immediate_Lang_0", 
## "RVLT_Delayed_Lang_0", "MAT_Lang_0", "Animal_Fluency_Lang_0", 
## "PASE_TOTAL_0", "PASE_Q1B_0", "RSTLS_Sleep_0")

Table1_Final<-CreateTableOne(vars=Baseline, data=BL.data)
print(Table1_Final,contDigits=2,missing=TRUE,quote=TRUE)

##                                               ""
##  ""                                            "Overall"        "Missing"
##   "n"                                          " 11355"         "    "   
##   "Age_0 (mean (SD))"                          " 61.62 (10.08)" " 0.0"   
##   "Sex_0 = M (%)"                              "  5467 (48.1) " " 0.0"   
##   "BMI_0 (mean (SD))"                          " 27.50 (5.10)"  " 0.5"   
##   "Ethnicity_0 = White (%)"                    " 11043 (97.3) " " 0.0"   
##   "Relationship_status_0 (%)"                  " "              " 0.0"   
##   "   Divorced"                                "   997 ( 8.8) " "    "   
##   "   Married"                                 "  8210 (72.3) " "    "   
##   "   Separated"                               "   290 ( 2.6) " "    "   
##   "   Single"                                  "   852 ( 7.5) " "    "   
##   "   Widowed"                                 "  1001 ( 8.8) " "    "   
##   "Education4_0 (%)"                           " "              " 0.0"   
##   "   College Degree or Higher"                "  8322 (73.3) " "    "   
##   "   High School Diploma"                     "  1449 (12.8) " "    "   
##   "   Less than High School Diploma"           "   749 ( 6.6) " "    "   
##   "   Some College"                            "   835 ( 7.4) " "    "   
##   "Income_Level_0 (%)"                         " "              " 3.5"   
##   "   <$20k"                                   "  1764 (16.1) " "    "   
##   "   >$150k"                                  "   428 ( 3.9) " "    "   
##   "   $100-150k"                               "   836 ( 7.6) " "    "   
##   "   $20-50k"                                 "  4349 (39.7) " "    "   
##   "   $50-100k"                                "  3584 (32.7) " "    "   
##   "Living_status_0 (%)"                        " "              " 0.0"   
##   "   Apartment/Condo/Townhome"                "  1342 (11.8) " "    "   
##   "   Assisted Living"                         "    61 ( 0.5) " "    "   
##   "   House"                                   "  9852 (86.8) " "    "   
##   "   Other"                                   "   100 ( 0.9) " "    "   
##   "Alcohol_0 (%)"                              " "              " 3.1"   
##   "   Non-drinker"                             "  1154 (10.5) " "    "   
##   "   Occasional drinker"                      "  1729 (15.7) " "    "   
##   "   Regular drinker (at least once a month)" "  8124 (73.8) " "    "   
##   "Smoking_Status_0 (%)"                       " "              " 0.4"   
##   "   Daily Smoker"                            "   744 ( 6.6) " "    "   
##   "   Former Smoker"                           "  6830 (60.4) " "    "   
##   "   Never Smoked"                            "  3541 (31.3) " "    "   
##   "   Occasional Smoker"                       "   189 ( 1.7) " "    "   
##   "CESD_10_0 (mean (SD))"                      "  4.96 (4.36)"  " 0.3"   
##   "Anxiety_0 = Yes (%)"                        "   718 ( 6.3) " " 0.1"   
##   "Mood_Disord_0 = Yes (%)"                    "  1508 (13.3) " " 0.1"   
##   "Chronic_conditions_0 (mean (SD))"           "  2.77 (2.26)"  " 3.8"   
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.44 (3.81)"  " 0.0"   
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.71 (3.71)"  " 0.0"   
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 10.29 (3.08)"  " 0.0"   
##   "MAT_Normed_0 (mean (SD))"                   "  9.95 (3.46)"  " 0.0"   
##   "RVLT_Immediate_Lang_0 = French (%)"         "  1962 (17.3) " " 0.0"   
##   "RVLT_Delayed_Lang_0 = French (%)"           "  1962 (17.3) " " 0.0"   
##   "MAT_Lang_0 = French (%)"                    "  1962 (17.3) " " 0.0"   
##   "Animal_Fluency_Lang_0 = French (%)"         "  1962 (17.3) " " 0.0"   
##   "PASE_TOTAL_0 (mean (SD))"                   "168.93 (78.96)" "19.1"   
##   "PASE_Q1B_0 = 1 (%)"                         "  4305 (38.6) " " 1.8"   
##   "RSTLS_Sleep_0 = 1 (%)"                      "  3741 (33.0) " " 0.2"

Final baseline sample stratified by whether FU2 data was collected before (N= 6,174) or after (N= 5,181) the start of the COVID-19 pandemic

Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.data)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)

##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected after COVID-19"
##   "n"                                          "  5181"                           
##   "Age_0 (mean (SD))"                          " 60.29 (10.54)"                   
##   "Sex_0 = M (%)"                              "  2856 (55.1) "                   
##   "BMI_0 (mean (SD))"                          " 27.52 (4.94)"                    
##   "Ethnicity_0 = White (%)"                    "  5025 (97.0) "                   
##   "Relationship_status_0 (%)"                  "  "                               
##   "   Divorced"                                "   408 ( 7.9) "                   
##   "   Married"                                 "  3841 (74.2) "                   
##   "   Separated"                               "   150 ( 2.9) "                   
##   "   Single"                                  "   383 ( 7.4) "                   
##   "   Widowed"                                 "   397 ( 7.7) "                   
##   "Education4_0 (%)"                           "  "                               
##   "   College Degree or Higher"                "  3605 (69.6) "                   
##   "   High School Diploma"                     "   776 (15.0) "                   
##   "   Less than High School Diploma"           "   411 ( 7.9) "                   
##   "   Some College"                            "   389 ( 7.5) "                   
##   "Income_Level_0 (%)"                         "  "                               
##   "   <$20k"                                   "   783 (15.6) "                   
##   "   >$150k"                                  "   239 ( 4.8) "                   
##   "   $100-150k"                               "   426 ( 8.5) "                   
##   "   $20-50k"                                 "  1874 (37.3) "                   
##   "   $50-100k"                                "  1700 (33.9) "                   
##   "Living_status_0 (%)"                        "  "                               
##   "   Apartment/Condo/Townhome"                "   579 (11.2) "                   
##   "   Assisted Living"                         "    24 ( 0.5) "                   
##   "   House"                                   "  4541 (87.6) "                   
##   "   Other"                                   "    37 ( 0.7) "                   
##   "Alcohol_0 (%)"                              "  "                               
##   "   Non-drinker"                             "   512 (10.2) "                   
##   "   Occasional drinker"                      "   784 (15.6) "                   
##   "   Regular drinker (at least once a month)" "  3731 (74.2) "                   
##   "Smoking_Status_0 (%)"                       "  "                               
##   "   Daily Smoker"                            "   361 ( 7.0) "                   
##   "   Former Smoker"                           "  3128 (60.6) "                   
##   "   Never Smoked"                            "  1579 (30.6) "                   
##   "   Occasional Smoker"                       "    90 ( 1.7) "                   
##   "CESD_10_0 (mean (SD))"                      "  5.08 (4.47)"                    
##   "Anxiety_0 = Yes (%)"                        "   335 ( 6.5) "                   
##   "Mood_Disord_0 = Yes (%)"                    "   698 (13.5) "                   
##   "Chronic_conditions_0 (mean (SD))"           "  2.60 (2.22)"                    
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.13 (3.76)"                    
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.45 (3.69)"                    
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 10.19 (3.09)"                    
##   "MAT_Normed_0 (mean (SD))"                   "  9.82 (3.46)"                    
##   "RVLT_Immediate_Lang_0 = French (%)"         "  1319 (25.5) "                   
##   "RVLT_Delayed_Lang_0 = French (%)"           "  1319 (25.5) "                   
##   "MAT_Lang_0 = French (%)"                    "  1319 (25.5) "                   
##   "Animal_Fluency_Lang_0 = French (%)"         "  1319 (25.5) "                   
##   "PASE_TOTAL_0 (mean (SD))"                   "179.56 (81.39)"                   
##   "PASE_Q1B_0 = 1 (%)"                         "  1875 (37.2) "                   
##   "RSTLS_Sleep_0 = 1 (%)"                      "  1710 (33.1) "                   
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected before COVID-19"
##   "n"                                          "  6174"                            
##   "Age_0 (mean (SD))"                          " 62.74 (9.53)"                     
##   "Sex_0 = M (%)"                              "  2611 (42.3) "                    
##   "BMI_0 (mean (SD))"                          " 27.48 (5.24)"                     
##   "Ethnicity_0 = White (%)"                    "  6018 (97.5) "                    
##   "Relationship_status_0 (%)"                  "  "                                
##   "   Divorced"                                "   589 ( 9.5) "                    
##   "   Married"                                 "  4369 (70.8) "                    
##   "   Separated"                               "   140 ( 2.3) "                    
##   "   Single"                                  "   469 ( 7.6) "                    
##   "   Widowed"                                 "   604 ( 9.8) "                    
##   "Education4_0 (%)"                           "  "                                
##   "   College Degree or Higher"                "  4717 (76.4) "                    
##   "   High School Diploma"                     "   673 (10.9) "                    
##   "   Less than High School Diploma"           "   338 ( 5.5) "                    
##   "   Some College"                            "   446 ( 7.2) "                    
##   "Income_Level_0 (%)"                         "  "                                
##   "   <$20k"                                   "   981 (16.5) "                    
##   "   >$150k"                                  "   189 ( 3.2) "                    
##   "   $100-150k"                               "   410 ( 6.9) "                    
##   "   $20-50k"                                 "  2475 (41.7) "                    
##   "   $50-100k"                                "  1884 (31.7) "                    
##   "Living_status_0 (%)"                        "  "                                
##   "   Apartment/Condo/Townhome"                "   763 (12.4) "                    
##   "   Assisted Living"                         "    37 ( 0.6) "                    
##   "   House"                                   "  5311 (86.0) "                    
##   "   Other"                                   "    63 ( 1.0) "                    
##   "Alcohol_0 (%)"                              "  "                                
##   "   Non-drinker"                             "   642 (10.7) "                    
##   "   Occasional drinker"                      "   945 (15.8) "                    
##   "   Regular drinker (at least once a month)" "  4393 (73.5) "                    
##   "Smoking_Status_0 (%)"                       "  "                                
##   "   Daily Smoker"                            "   383 ( 6.2) "                    
##   "   Former Smoker"                           "  3702 (60.2) "                    
##   "   Never Smoked"                            "  1962 (31.9) "                    
##   "   Occasional Smoker"                       "    99 ( 1.6) "                    
##   "CESD_10_0 (mean (SD))"                      "  4.85 (4.27)"                     
##   "Anxiety_0 = Yes (%)"                        "   383 ( 6.2) "                    
##   "Mood_Disord_0 = Yes (%)"                    "   810 (13.1) "                    
##   "Chronic_conditions_0 (mean (SD))"           "  2.91 (2.29)"                     
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.70 (3.84)"                     
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.93 (3.72)"                     
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 10.38 (3.06)"                     
##   "MAT_Normed_0 (mean (SD))"                   " 10.06 (3.46)"                     
##   "RVLT_Immediate_Lang_0 = French (%)"         "   643 (10.4) "                    
##   "RVLT_Delayed_Lang_0 = French (%)"           "   643 (10.4) "                    
##   "MAT_Lang_0 = French (%)"                    "   643 (10.4) "                    
##   "Animal_Fluency_Lang_0 = French (%)"         "   643 (10.4) "                    
##   "PASE_TOTAL_0 (mean (SD))"                   "160.16 (75.79)"                    
##   "PASE_Q1B_0 = 1 (%)"                         "  2430 (39.7) "                    
##   "RSTLS_Sleep_0 = 1 (%)"                      "  2031 (32.9) "                    
##                                               "Stratified by Pandemic"
##  ""                                            "p"      "test" "Missing"
##   "n"                                          ""       ""     "    "   
##   "Age_0 (mean (SD))"                          "<0.001" ""     " 0.0"   
##   "Sex_0 = M (%)"                              "<0.001" ""     " 0.0"   
##   "BMI_0 (mean (SD))"                          " 0.688" ""     " 0.5"   
##   "Ethnicity_0 = White (%)"                    " 0.130" ""     " 0.0"   
##   "Relationship_status_0 (%)"                  "<0.001" ""     " 0.0"   
##   "   Divorced"                                ""       ""     "    "   
##   "   Married"                                 ""       ""     "    "   
##   "   Separated"                               ""       ""     "    "   
##   "   Single"                                  ""       ""     "    "   
##   "   Widowed"                                 ""       ""     "    "   
##   "Education4_0 (%)"                           "<0.001" ""     " 0.0"   
##   "   College Degree or Higher"                ""       ""     "    "   
##   "   High School Diploma"                     ""       ""     "    "   
##   "   Less than High School Diploma"           ""       ""     "    "   
##   "   Some College"                            ""       ""     "    "   
##   "Income_Level_0 (%)"                         "<0.001" ""     " 3.5"   
##   "   <$20k"                                   ""       ""     "    "   
##   "   >$150k"                                  ""       ""     "    "   
##   "   $100-150k"                               ""       ""     "    "   
##   "   $20-50k"                                 ""       ""     "    "   
##   "   $50-100k"                                ""       ""     "    "   
##   "Living_status_0 (%)"                        " 0.043" ""     " 0.0"   
##   "   Apartment/Condo/Townhome"                ""       ""     "    "   
##   "   Assisted Living"                         ""       ""     "    "   
##   "   House"                                   ""       ""     "    "   
##   "   Other"                                   ""       ""     "    "   
##   "Alcohol_0 (%)"                              " 0.584" ""     " 3.1"   
##   "   Non-drinker"                             ""       ""     "    "   
##   "   Occasional drinker"                      ""       ""     "    "   
##   "   Regular drinker (at least once a month)" ""       ""     "    "   
##   "Smoking_Status_0 (%)"                       " 0.219" ""     " 0.4"   
##   "   Daily Smoker"                            ""       ""     "    "   
##   "   Former Smoker"                           ""       ""     "    "   
##   "   Never Smoked"                            ""       ""     "    "   
##   "   Occasional Smoker"                       ""       ""     "    "   
##   "CESD_10_0 (mean (SD))"                      " 0.005" ""     " 0.3"   
##   "Anxiety_0 = Yes (%)"                        " 0.589" ""     " 0.1"   
##   "Mood_Disord_0 = Yes (%)"                    " 0.604" ""     " 0.1"   
##   "Chronic_conditions_0 (mean (SD))"           "<0.001" ""     " 3.8"   
##   "RVLT_Immediate_Normed_0 (mean (SD))"        "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Normed_0 (mean (SD))"          "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 0.001" ""     " 0.0"   
##   "MAT_Normed_0 (mean (SD))"                   "<0.001" ""     " 0.0"   
##   "RVLT_Immediate_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Lang_0 = French (%)"           "<0.001" ""     " 0.0"   
##   "MAT_Lang_0 = French (%)"                    "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "PASE_TOTAL_0 (mean (SD))"                   "<0.001" ""     "19.1"   
##   "PASE_Q1B_0 = 1 (%)"                         " 0.008" ""     " 1.8"   
##   "RSTLS_Sleep_0 = 1 (%)"                      " 0.903" ""     " 0.2"

Stratify Results By Age, Sex, and Pandemic Status

BL.data$Age_sex<-NA
BL.data$Age_sex[BL.data$Age_0<=64 & BL.data$Sex_0 == "M"]<-"Males 45-64"
BL.data$Age_sex[BL.data$Age_0<=64 & BL.data$Sex_0 == "F"]<-"Females 45-64"
BL.data$Age_sex[BL.data$Age_0>64 & BL.data$Sex_0 == "M"]<-"Males 65+"
BL.data$Age_sex[BL.data$Age_0>64 & BL.data$Sex_0 == "F"]<-"Females 65+"

BL.MalesYoung<-subset(BL.data, Age_sex=="Males 45-64")
BL.FemalesYoung<-subset(BL.data, Age_sex=="Females 45-64")
BL.MalesOld<-subset(BL.data, Age_sex=="Males 65+")
BL.FemalesOld<-subset(BL.data, Age_sex=="Females 65+")

Males 45-64

Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.MalesYoung)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)

##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected after COVID-19"
##   "n"                                          "  2032"                           
##   "Age_0 (mean (SD))"                          " 54.12 (5.36)"                    
##   "Sex_0 = M (%)"                              "  2032 (100.0) "                  
##   "BMI_0 (mean (SD))"                          " 27.98 (4.44)"                    
##   "Ethnicity_0 = White (%)"                    "  1947 ( 95.8) "                  
##   "Relationship_status_0 (%)"                  "  "                               
##   "   Divorced"                                "   126 (  6.2) "                  
##   "   Married"                                 "  1656 ( 81.5) "                  
##   "   Separated"                               "    64 (  3.2) "                  
##   "   Single"                                  "   163 (  8.0) "                  
##   "   Widowed"                                 "    22 (  1.1) "                  
##   "Education4_0 (%)"                           "  "                               
##   "   College Degree or Higher"                "  1555 ( 76.5) "                  
##   "   High School Diploma"                     "   248 ( 12.2) "                  
##   "   Less than High School Diploma"           "    95 (  4.7) "                  
##   "   Some College"                            "   134 (  6.6) "                  
##   "Income_Level_0 (%)"                         "  "                               
##   "   <$20k"                                   "   123 (  6.2) "                  
##   "   >$150k"                                  "   188 (  9.4) "                  
##   "   $100-150k"                               "   297 ( 14.9) "                  
##   "   $20-50k"                                 "   517 ( 25.9) "                  
##   "   $50-100k"                                "   873 ( 43.7) "                  
##   "Living_status_0 (%)"                        "  "                               
##   "   Apartment/Condo/Townhome"                "   146 (  7.2) "                  
##   "   Assisted Living"                         "     2 (  0.1) "                  
##   "   House"                                   "  1872 ( 92.1) "                  
##   "   Other"                                   "    12 (  0.6) "                  
##   "Alcohol_0 (%)"                              "  "                               
##   "   Non-drinker"                             "   174 (  8.7) "                  
##   "   Occasional drinker"                      "   223 ( 11.2) "                  
##   "   Regular drinker (at least once a month)" "  1597 ( 80.1) "                  
##   "Smoking_Status_0 (%)"                       "  "                               
##   "   Daily Smoker"                            "   168 (  8.3) "                  
##   "   Former Smoker"                           "  1211 ( 59.8) "                  
##   "   Never Smoked"                            "   605 ( 29.9) "                  
##   "   Occasional Smoker"                       "    40 (  2.0) "                  
##   "CESD_10_0 (mean (SD))"                      "  4.82 (4.40)"                    
##   "Anxiety_0 = Yes (%)"                        "   100 (  4.9) "                  
##   "Mood_Disord_0 = Yes (%)"                    "   257 ( 12.7) "                  
##   "Chronic_conditions_0 (mean (SD))"           "  1.91 (1.76)"                    
##   "RVLT_Immediate_Normed_0 (mean (SD))"        "  9.73 (3.52)"                    
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.02 (3.56)"                    
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 10.17 (3.15)"                    
##   "MAT_Normed_0 (mean (SD))"                   "  9.75 (3.41)"                    
##   "RVLT_Immediate_Lang_0 = French (%)"         "   485 ( 23.9) "                  
##   "RVLT_Delayed_Lang_0 = French (%)"           "   485 ( 23.9) "                  
##   "MAT_Lang_0 = French (%)"                    "   485 ( 23.9) "                  
##   "Animal_Fluency_Lang_0 = French (%)"         "   485 ( 23.9) "                  
##   "PASE_TOTAL_0 (mean (SD))"                   "217.41 (81.01)"                   
##   "PASE_Q1B_0 = 1 (%)"                         "   630 ( 31.9) "                  
##   "RSTLS_Sleep_0 = 1 (%)"                      "   634 ( 31.2) "                  
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected before COVID-19"
##   "n"                                          "  1424"                            
##   "Age_0 (mean (SD))"                          " 56.68 (5.24)"                     
##   "Sex_0 = M (%)"                              "  1424 (100.0) "                   
##   "BMI_0 (mean (SD))"                          " 28.10 (4.65)"                     
##   "Ethnicity_0 = White (%)"                    "  1381 ( 97.0) "                   
##   "Relationship_status_0 (%)"                  "  "                                
##   "   Divorced"                                "    73 (  5.1) "                   
##   "   Married"                                 "  1152 ( 81.0) "                   
##   "   Separated"                               "    43 (  3.0) "                   
##   "   Single"                                  "   127 (  8.9) "                   
##   "   Widowed"                                 "    28 (  2.0) "                   
##   "Education4_0 (%)"                           "  "                                
##   "   College Degree or Higher"                "  1125 ( 79.0) "                   
##   "   High School Diploma"                     "   147 ( 10.3) "                   
##   "   Less than High School Diploma"           "    50 (  3.5) "                   
##   "   Some College"                            "   102 (  7.2) "                   
##   "Income_Level_0 (%)"                         "  "                                
##   "   <$20k"                                   "    94 (  6.8) "                   
##   "   >$150k"                                  "    92 (  6.6) "                   
##   "   $100-150k"                               "   207 ( 14.9) "                   
##   "   $20-50k"                                 "   414 ( 29.7) "                   
##   "   $50-100k"                                "   585 ( 42.0) "                   
##   "Living_status_0 (%)"                        "  "                                
##   "   Apartment/Condo/Townhome"                "   122 (  8.6) "                   
##   "   Assisted Living"                         "     1 (  0.1) "                   
##   "   House"                                   "  1290 ( 90.6) "                   
##   "   Other"                                   "    11 (  0.8) "                   
##   "Alcohol_0 (%)"                              "  "                                
##   "   Non-drinker"                             "   131 (  9.3) "                   
##   "   Occasional drinker"                      "   136 (  9.7) "                   
##   "   Regular drinker (at least once a month)" "  1135 ( 81.0) "                   
##   "Smoking_Status_0 (%)"                       "  "                                
##   "   Daily Smoker"                            "   105 (  7.4) "                   
##   "   Former Smoker"                           "   874 ( 61.7) "                   
##   "   Never Smoked"                            "   401 ( 28.3) "                   
##   "   Occasional Smoker"                       "    37 (  2.6) "                   
##   "CESD_10_0 (mean (SD))"                      "  4.51 (4.01)"                     
##   "Anxiety_0 = Yes (%)"                        "    68 (  4.8) "                   
##   "Mood_Disord_0 = Yes (%)"                    "   144 ( 10.1) "                   
##   "Chronic_conditions_0 (mean (SD))"           "  2.11 (1.75)"                     
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.40 (3.86)"                     
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.60 (3.77)"                     
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 10.32 (3.29)"                     
##   "MAT_Normed_0 (mean (SD))"                   " 10.02 (3.38)"                     
##   "RVLT_Immediate_Lang_0 = French (%)"         "   147 ( 10.3) "                   
##   "RVLT_Delayed_Lang_0 = French (%)"           "   147 ( 10.3) "                   
##   "MAT_Lang_0 = French (%)"                    "   147 ( 10.3) "                   
##   "Animal_Fluency_Lang_0 = French (%)"         "   147 ( 10.3) "                   
##   "PASE_TOTAL_0 (mean (SD))"                   "199.82 (78.37)"                    
##   "PASE_Q1B_0 = 1 (%)"                         "   510 ( 36.3) "                   
##   "RSTLS_Sleep_0 = 1 (%)"                      "   422 ( 29.7) "                   
##                                               "Stratified by Pandemic"
##  ""                                            "p"      "test" "Missing"
##   "n"                                          ""       ""     "    "   
##   "Age_0 (mean (SD))"                          "<0.001" ""     " 0.0"   
##   "Sex_0 = M (%)"                              "    NA" ""     " 0.0"   
##   "BMI_0 (mean (SD))"                          " 0.437" ""     " 0.2"   
##   "Ethnicity_0 = White (%)"                    " 0.091" ""     " 0.0"   
##   "Relationship_status_0 (%)"                  " 0.132" ""     " 0.1"   
##   "   Divorced"                                ""       ""     "    "   
##   "   Married"                                 ""       ""     "    "   
##   "   Separated"                               ""       ""     "    "   
##   "   Single"                                  ""       ""     "    "   
##   "   Widowed"                                 ""       ""     "    "   
##   "Education4_0 (%)"                           " 0.096" ""     " 0.0"   
##   "   College Degree or Higher"                ""       ""     "    "   
##   "   High School Diploma"                     ""       ""     "    "   
##   "   Less than High School Diploma"           ""       ""     "    "   
##   "   Some College"                            ""       ""     "    "   
##   "Income_Level_0 (%)"                         " 0.010" ""     " 1.9"   
##   "   <$20k"                                   ""       ""     "    "   
##   "   >$150k"                                  ""       ""     "    "   
##   "   $100-150k"                               ""       ""     "    "   
##   "   $20-50k"                                 ""       ""     "    "   
##   "   $50-100k"                                ""       ""     "    "   
##   "Living_status_0 (%)"                        " 0.428" ""     " 0.0"   
##   "   Apartment/Condo/Townhome"                ""       ""     "    "   
##   "   Assisted Living"                         ""       ""     "    "   
##   "   House"                                   ""       ""     "    "   
##   "   Other"                                   ""       ""     "    "   
##   "Alcohol_0 (%)"                              " 0.343" ""     " 1.7"   
##   "   Non-drinker"                             ""       ""     "    "   
##   "   Occasional drinker"                      ""       ""     "    "   
##   "   Regular drinker (at least once a month)" ""       ""     "    "   
##   "Smoking_Status_0 (%)"                       " 0.317" ""     " 0.4"   
##   "   Daily Smoker"                            ""       ""     "    "   
##   "   Former Smoker"                           ""       ""     "    "   
##   "   Never Smoked"                            ""       ""     "    "   
##   "   Occasional Smoker"                       ""       ""     "    "   
##   "CESD_10_0 (mean (SD))"                      " 0.032" ""     " 0.2"   
##   "Anxiety_0 = Yes (%)"                        " 0.905" ""     " 0.0"   
##   "Mood_Disord_0 = Yes (%)"                    " 0.026" ""     " 0.1"   
##   "Chronic_conditions_0 (mean (SD))"           " 0.001" ""     " 2.7"   
##   "RVLT_Immediate_Normed_0 (mean (SD))"        "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Normed_0 (mean (SD))"          "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 0.202" ""     " 0.0"   
##   "MAT_Normed_0 (mean (SD))"                   " 0.022" ""     " 0.0"   
##   "RVLT_Immediate_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Lang_0 = French (%)"           "<0.001" ""     " 0.0"   
##   "MAT_Lang_0 = French (%)"                    "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "PASE_TOTAL_0 (mean (SD))"                   "<0.001" ""     "17.5"   
##   "PASE_Q1B_0 = 1 (%)"                         " 0.008" ""     " 2.3"   
##   "RSTLS_Sleep_0 = 1 (%)"                      " 0.348" ""     " 0.1"

Females 45-64

Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.FemalesYoung)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)

##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected after COVID-19"
##   "n"                                          "  1498"                           
##   "Age_0 (mean (SD))"                          " 54.09 (5.34)"                    
##   "Sex_0 = F (%)"                              "  1498 (100.0) "                  
##   "BMI_0 (mean (SD))"                          " 27.18 (5.75)"                    
##   "Ethnicity_0 = White (%)"                    "  1460 ( 97.5) "                  
##   "Relationship_status_0 (%)"                  "  "                               
##   "   Divorced"                                "   137 (  9.1) "                  
##   "   Married"                                 "  1107 ( 73.9) "                  
##   "   Separated"                               "    56 (  3.7) "                  
##   "   Single"                                  "   141 (  9.4) "                  
##   "   Widowed"                                 "    57 (  3.8) "                  
##   "Education4_0 (%)"                           "  "                               
##   "   College Degree or Higher"                "  1064 ( 71.0) "                  
##   "   High School Diploma"                     "   248 ( 16.6) "                  
##   "   Less than High School Diploma"           "    65 (  4.3) "                  
##   "   Some College"                            "   121 (  8.1) "                  
##   "Income_Level_0 (%)"                         "  "                               
##   "   <$20k"                                   "   320 ( 22.2) "                  
##   "   >$150k"                                  "    26 (  1.8) "                  
##   "   $100-150k"                               "    71 (  4.9) "                  
##   "   $20-50k"                                 "   550 ( 38.2) "                  
##   "   $50-100k"                                "   473 ( 32.8) "                  
##   "Living_status_0 (%)"                        "  "                               
##   "   Apartment/Condo/Townhome"                "   119 (  7.9) "                  
##   "   Assisted Living"                         "     1 (  0.1) "                  
##   "   House"                                   "  1371 ( 91.5) "                  
##   "   Other"                                   "     7 (  0.5) "                  
##   "Alcohol_0 (%)"                              "  "                               
##   "   Non-drinker"                             "   140 (  9.6) "                  
##   "   Occasional drinker"                      "   283 ( 19.4) "                  
##   "   Regular drinker (at least once a month)" "  1039 ( 71.1) "                  
##   "Smoking_Status_0 (%)"                       "  "                               
##   "   Daily Smoker"                            "   138 (  9.3) "                  
##   "   Former Smoker"                           "   838 ( 56.2) "                  
##   "   Never Smoked"                            "   479 ( 32.1) "                  
##   "   Occasional Smoker"                       "    36 (  2.4) "                  
##   "CESD_10_0 (mean (SD))"                      "  5.68 (4.91)"                    
##   "Anxiety_0 = Yes (%)"                        "   159 ( 10.6) "                  
##   "Mood_Disord_0 = Yes (%)"                    "   298 ( 19.9) "                  
##   "Chronic_conditions_0 (mean (SD))"           "  2.36 (2.05)"                    
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.13 (3.67)"                    
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.31 (3.50)"                    
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 10.23 (3.15)"                    
##   "MAT_Normed_0 (mean (SD))"                   "  9.92 (3.32)"                    
##   "RVLT_Immediate_Lang_0 = French (%)"         "   396 ( 26.4) "                  
##   "RVLT_Delayed_Lang_0 = French (%)"           "   396 ( 26.4) "                  
##   "MAT_Lang_0 = French (%)"                    "   396 ( 26.4) "                  
##   "Animal_Fluency_Lang_0 = French (%)"         "   396 ( 26.4) "                  
##   "PASE_TOTAL_0 (mean (SD))"                   "182.11 (73.93)"                   
##   "PASE_Q1B_0 = 1 (%)"                         "   507 ( 35.1) "                  
##   "RSTLS_Sleep_0 = 1 (%)"                      "   582 ( 39.0) "                  
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected before COVID-19"
##   "n"                                          "  2246"                            
##   "Age_0 (mean (SD))"                          " 55.97 (5.32)"                     
##   "Sex_0 = F (%)"                              "  2246 (100.0) "                   
##   "BMI_0 (mean (SD))"                          " 27.52 (6.16)"                     
##   "Ethnicity_0 = White (%)"                    "  2188 ( 97.4) "                   
##   "Relationship_status_0 (%)"                  "  "                                
##   "   Divorced"                                "   250 ( 11.1) "                   
##   "   Married"                                 "  1620 ( 72.1) "                   
##   "   Separated"                               "    62 (  2.8) "                   
##   "   Single"                                  "   213 (  9.5) "                   
##   "   Widowed"                                 "   101 (  4.5) "                   
##   "Education4_0 (%)"                           "  "                                
##   "   College Degree or Higher"                "  1747 ( 77.8) "                   
##   "   High School Diploma"                     "   244 ( 10.9) "                   
##   "   Less than High School Diploma"           "    82 (  3.7) "                   
##   "   Some College"                            "   173 (  7.7) "                   
##   "Income_Level_0 (%)"                         "  "                                
##   "   <$20k"                                   "   483 ( 22.3) "                   
##   "   >$150k"                                  "    41 (  1.9) "                   
##   "   $100-150k"                               "   113 (  5.2) "                   
##   "   $20-50k"                                 "   847 ( 39.1) "                   
##   "   $50-100k"                                "   680 ( 31.4) "                   
##   "Living_status_0 (%)"                        "  "                                
##   "   Apartment/Condo/Townhome"                "   217 (  9.7) "                   
##   "   Assisted Living"                         "     4 (  0.2) "                   
##   "   House"                                   "  2006 ( 89.3) "                   
##   "   Other"                                   "    19 (  0.8) "                   
##   "Alcohol_0 (%)"                              "  "                                
##   "   Non-drinker"                             "   224 ( 10.2) "                   
##   "   Occasional drinker"                      "   422 ( 19.3) "                   
##   "   Regular drinker (at least once a month)" "  1546 ( 70.5) "                   
##   "Smoking_Status_0 (%)"                       "  "                                
##   "   Daily Smoker"                            "   187 (  8.3) "                   
##   "   Former Smoker"                           "  1200 ( 53.5) "                   
##   "   Never Smoked"                            "   810 ( 36.1) "                   
##   "   Occasional Smoker"                       "    45 (  2.0) "                   
##   "CESD_10_0 (mean (SD))"                      "  5.26 (4.56)"                     
##   "Anxiety_0 = Yes (%)"                        "   180 (  8.0) "                   
##   "Mood_Disord_0 = Yes (%)"                    "   423 ( 18.8) "                   
##   "Chronic_conditions_0 (mean (SD))"           "  2.59 (2.14)"                     
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.65 (3.81)"                     
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.85 (3.61)"                     
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 10.42 (3.13)"                     
##   "MAT_Normed_0 (mean (SD))"                   " 10.16 (3.38)"                     
##   "RVLT_Immediate_Lang_0 = French (%)"         "   304 ( 13.5) "                   
##   "RVLT_Delayed_Lang_0 = French (%)"           "   304 ( 13.5) "                   
##   "MAT_Lang_0 = French (%)"                    "   304 ( 13.5) "                   
##   "Animal_Fluency_Lang_0 = French (%)"         "   304 ( 13.5) "                   
##   "PASE_TOTAL_0 (mean (SD))"                   "170.96 (72.48)"                    
##   "PASE_Q1B_0 = 1 (%)"                         "   830 ( 37.3) "                   
##   "RSTLS_Sleep_0 = 1 (%)"                      "   829 ( 37.0) "                   
##                                               "Stratified by Pandemic"
##  ""                                            "p"      "test" "Missing"
##   "n"                                          ""       ""     "    "   
##   "Age_0 (mean (SD))"                          "<0.001" ""     " 0.0"   
##   "Sex_0 = F (%)"                              "    NA" ""     " 0.0"   
##   "BMI_0 (mean (SD))"                          " 0.084" ""     " 0.9"   
##   "Ethnicity_0 = White (%)"                    " 1.000" ""     " 0.0"   
##   "Relationship_status_0 (%)"                  " 0.109" ""     " 0.0"   
##   "   Divorced"                                ""       ""     "    "   
##   "   Married"                                 ""       ""     "    "   
##   "   Separated"                               ""       ""     "    "   
##   "   Single"                                  ""       ""     "    "   
##   "   Widowed"                                 ""       ""     "    "   
##   "Education4_0 (%)"                           "<0.001" ""     " 0.0"   
##   "   College Degree or Higher"                ""       ""     "    "   
##   "   High School Diploma"                     ""       ""     "    "   
##   "   Less than High School Diploma"           ""       ""     "    "   
##   "   Some College"                            ""       ""     "    "   
##   "Income_Level_0 (%)"                         " 0.920" ""     " 3.7"   
##   "   <$20k"                                   ""       ""     "    "   
##   "   >$150k"                                  ""       ""     "    "   
##   "   $100-150k"                               ""       ""     "    "   
##   "   $20-50k"                                 ""       ""     "    "   
##   "   $50-100k"                                ""       ""     "    "   
##   "Living_status_0 (%)"                        " 0.105" ""     " 0.0"   
##   "   Apartment/Condo/Townhome"                ""       ""     "    "   
##   "   Assisted Living"                         ""       ""     "    "   
##   "   House"                                   ""       ""     "    "   
##   "   Other"                                   ""       ""     "    "   
##   "Alcohol_0 (%)"                              " 0.817" ""     " 2.4"   
##   "   Non-drinker"                             ""       ""     "    "   
##   "   Occasional drinker"                      ""       ""     "    "   
##   "   Regular drinker (at least once a month)" ""       ""     "    "   
##   "Smoking_Status_0 (%)"                       " 0.076" ""     " 0.3"   
##   "   Daily Smoker"                            ""       ""     "    "   
##   "   Former Smoker"                           ""       ""     "    "   
##   "   Never Smoked"                            ""       ""     "    "   
##   "   Occasional Smoker"                       ""       ""     "    "   
##   "CESD_10_0 (mean (SD))"                      " 0.008" ""     " 0.2"   
##   "Anxiety_0 = Yes (%)"                        " 0.007" ""     " 0.1"   
##   "Mood_Disord_0 = Yes (%)"                    " 0.443" ""     " 0.1"   
##   "Chronic_conditions_0 (mean (SD))"           " 0.001" ""     " 3.3"   
##   "RVLT_Immediate_Normed_0 (mean (SD))"        "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Normed_0 (mean (SD))"          "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 0.062" ""     " 0.0"   
##   "MAT_Normed_0 (mean (SD))"                   " 0.029" ""     " 0.0"   
##   "RVLT_Immediate_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Lang_0 = French (%)"           "<0.001" ""     " 0.0"   
##   "MAT_Lang_0 = French (%)"                    "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "PASE_TOTAL_0 (mean (SD))"                   "<0.001" ""     "18.8"   
##   "PASE_Q1B_0 = 1 (%)"                         " 0.189" ""     " 2.0"   
##   "RSTLS_Sleep_0 = 1 (%)"                      " 0.231" ""     " 0.2"

Males 65+

Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.MalesOld)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)

##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected after COVID-19"
##   "n"                                          "   824"                           
##   "Age_0 (mean (SD))"                          " 73.50 (5.55)"                    
##   "Sex_0 = M (%)"                              "   824 (100.0) "                  
##   "BMI_0 (mean (SD))"                          " 27.23 (4.10)"                    
##   "Ethnicity_0 = White (%)"                    "   802 ( 97.3) "                  
##   "Relationship_status_0 (%)"                  "   "                              
##   "   Divorced"                                "    47 (  5.7) "                  
##   "   Married"                                 "   663 ( 80.6) "                  
##   "   Separated"                               "    18 (  2.2) "                  
##   "   Single"                                  "    33 (  4.0) "                  
##   "   Widowed"                                 "    62 (  7.5) "                  
##   "Education4_0 (%)"                           "   "                              
##   "   College Degree or Higher"                "   542 ( 65.8) "                  
##   "   High School Diploma"                     "   113 ( 13.7) "                  
##   "   Less than High School Diploma"           "   103 ( 12.5) "                  
##   "   Some College"                            "    66 (  8.0) "                  
##   "Income_Level_0 (%)"                         "   "                              
##   "   <$20k"                                   "    79 (  9.8) "                  
##   "   >$150k"                                  "    19 (  2.4) "                  
##   "   $100-150k"                               "    48 (  6.0) "                  
##   "   $20-50k"                                 "   408 ( 50.7) "                  
##   "   $50-100k"                                "   250 ( 31.1) "                  
##   "Living_status_0 (%)"                        "   "                              
##   "   Apartment/Condo/Townhome"                "   125 ( 15.2) "                  
##   "   Assisted Living"                         "     4 (  0.5) "                  
##   "   House"                                   "   690 ( 83.7) "                  
##   "   Other"                                   "     5 (  0.6) "                  
##   "Alcohol_0 (%)"                              "   "                              
##   "   Non-drinker"                             "    89 ( 11.1) "                  
##   "   Occasional drinker"                      "    92 ( 11.5) "                  
##   "   Regular drinker (at least once a month)" "   618 ( 77.3) "                  
##   "Smoking_Status_0 (%)"                       "   "                              
##   "   Daily Smoker"                            "    22 (  2.7) "                  
##   "   Former Smoker"                           "   620 ( 75.7) "                  
##   "   Never Smoked"                            "   171 ( 20.9) "                  
##   "   Occasional Smoker"                       "     6 (  0.7) "                  
##   "CESD_10_0 (mean (SD))"                      "  4.39 (3.83)"                    
##   "Anxiety_0 = Yes (%)"                        "    22 (  2.7) "                  
##   "Mood_Disord_0 = Yes (%)"                    "    46 (  5.6) "                  
##   "Chronic_conditions_0 (mean (SD))"           "  3.24 (2.21)"                    
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.32 (4.11)"                    
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.83 (3.91)"                    
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 10.16 (2.88)"                    
##   "MAT_Normed_0 (mean (SD))"                   "  9.69 (3.57)"                    
##   "RVLT_Immediate_Lang_0 = French (%)"         "   220 ( 26.7) "                  
##   "RVLT_Delayed_Lang_0 = French (%)"           "   220 ( 26.7) "                  
##   "MAT_Lang_0 = French (%)"                    "   220 ( 26.7) "                  
##   "Animal_Fluency_Lang_0 = French (%)"         "   220 ( 26.7) "                  
##   "PASE_TOTAL_0 (mean (SD))"                   "138.16 (60.96)"                   
##   "PASE_Q1B_0 = 1 (%)"                         "   355 ( 44.3) "                  
##   "RSTLS_Sleep_0 = 1 (%)"                      "   232 ( 28.3) "                  
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected before COVID-19"
##   "n"                                          "  1187"                            
##   "Age_0 (mean (SD))"                          " 72.09 (5.35)"                     
##   "Sex_0 = M (%)"                              "  1187 (100.0) "                   
##   "BMI_0 (mean (SD))"                          " 27.19 (3.85)"                     
##   "Ethnicity_0 = White (%)"                    "  1156 ( 97.4) "                   
##   "Relationship_status_0 (%)"                  "  "                                
##   "   Divorced"                                "    73 (  6.1) "                   
##   "   Married"                                 "   937 ( 78.9) "                   
##   "   Separated"                               "    16 (  1.3) "                   
##   "   Single"                                  "    50 (  4.2) "                   
##   "   Widowed"                                 "   111 (  9.4) "                   
##   "Education4_0 (%)"                           "  "                                
##   "   College Degree or Higher"                "   870 ( 73.3) "                   
##   "   High School Diploma"                     "   125 ( 10.5) "                   
##   "   Less than High School Diploma"           "   100 (  8.4) "                   
##   "   Some College"                            "    92 (  7.8) "                   
##   "Income_Level_0 (%)"                         "  "                                
##   "   <$20k"                                   "    89 (  7.7) "                   
##   "   >$150k"                                  "    47 (  4.1) "                   
##   "   $100-150k"                               "    71 (  6.1) "                   
##   "   $20-50k"                                 "   547 ( 47.3) "                   
##   "   $50-100k"                                "   403 ( 34.8) "                   
##   "Living_status_0 (%)"                        "  "                                
##   "   Apartment/Condo/Townhome"                "   152 ( 12.8) "                   
##   "   Assisted Living"                         "     9 (  0.8) "                   
##   "   House"                                   "  1009 ( 85.0) "                   
##   "   Other"                                   "    17 (  1.4) "                   
##   "Alcohol_0 (%)"                              "  "                                
##   "   Non-drinker"                             "   124 ( 10.8) "                   
##   "   Occasional drinker"                      "   118 ( 10.2) "                   
##   "   Regular drinker (at least once a month)" "   911 ( 79.0) "                   
##   "Smoking_Status_0 (%)"                       "  "                                
##   "   Daily Smoker"                            "    39 (  3.3) "                   
##   "   Former Smoker"                           "   865 ( 73.4) "                   
##   "   Never Smoked"                            "   267 ( 22.7) "                   
##   "   Occasional Smoker"                       "     7 (  0.6) "                   
##   "CESD_10_0 (mean (SD))"                      "  4.17 (3.69)"                     
##   "Anxiety_0 = Yes (%)"                        "    45 (  3.8) "                   
##   "Mood_Disord_0 = Yes (%)"                    "    87 (  7.3) "                   
##   "Chronic_conditions_0 (mean (SD))"           "  3.32 (2.30)"                     
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.62 (3.68)"                     
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 10.91 (3.67)"                     
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 10.26 (2.87)"                     
##   "MAT_Normed_0 (mean (SD))"                   " 10.04 (3.53)"                     
##   "RVLT_Immediate_Lang_0 = French (%)"         "    88 (  7.4) "                   
##   "RVLT_Delayed_Lang_0 = French (%)"           "    88 (  7.4) "                   
##   "MAT_Lang_0 = French (%)"                    "    88 (  7.4) "                   
##   "Animal_Fluency_Lang_0 = French (%)"         "    88 (  7.4) "                   
##   "PASE_TOTAL_0 (mean (SD))"                   "136.93 (63.79)"                    
##   "PASE_Q1B_0 = 1 (%)"                         "   541 ( 45.8) "                   
##   "RSTLS_Sleep_0 = 1 (%)"                      "   345 ( 29.1) "                   
##                                               "Stratified by Pandemic"
##  ""                                            "p"      "test" "Missing"
##   "n"                                          ""       ""     "    "   
##   "Age_0 (mean (SD))"                          "<0.001" ""     " 0.0"   
##   "Sex_0 = M (%)"                              "    NA" ""     " 0.0"   
##   "BMI_0 (mean (SD))"                          " 0.834" ""     " 0.2"   
##   "Ethnicity_0 = White (%)"                    " 1.000" ""     " 0.0"   
##   "Relationship_status_0 (%)"                  " 0.373" ""     " 0.0"   
##   "   Divorced"                                ""       ""     "    "   
##   "   Married"                                 ""       ""     "    "   
##   "   Separated"                               ""       ""     "    "   
##   "   Single"                                  ""       ""     "    "   
##   "   Widowed"                                 ""       ""     "    "   
##   "Education4_0 (%)"                           " 0.001" ""     " 0.0"   
##   "   College Degree or Higher"                ""       ""     "    "   
##   "   High School Diploma"                     ""       ""     "    "   
##   "   Less than High School Diploma"           ""       ""     "    "   
##   "   Some College"                            ""       ""     "    "   
##   "Income_Level_0 (%)"                         " 0.044" ""     " 2.5"   
##   "   <$20k"                                   ""       ""     "    "   
##   "   >$150k"                                  ""       ""     "    "   
##   "   $100-150k"                               ""       ""     "    "   
##   "   $20-50k"                                 ""       ""     "    "   
##   "   $50-100k"                                ""       ""     "    "   
##   "Living_status_0 (%)"                        " 0.130" ""     " 0.0"   
##   "   Apartment/Condo/Townhome"                ""       ""     "    "   
##   "   Assisted Living"                         ""       ""     "    "   
##   "   House"                                   ""       ""     "    "   
##   "   Other"                                   ""       ""     "    "   
##   "Alcohol_0 (%)"                              " 0.622" ""     " 2.9"   
##   "   Non-drinker"                             ""       ""     "    "   
##   "   Occasional drinker"                      ""       ""     "    "   
##   "   Regular drinker (at least once a month)" ""       ""     "    "   
##   "Smoking_Status_0 (%)"                       " 0.616" ""     " 0.7"   
##   "   Daily Smoker"                            ""       ""     "    "   
##   "   Former Smoker"                           ""       ""     "    "   
##   "   Never Smoked"                            ""       ""     "    "   
##   "   Occasional Smoker"                       ""       ""     "    "   
##   "CESD_10_0 (mean (SD))"                      " 0.199" ""     " 0.5"   
##   "Anxiety_0 = Yes (%)"                        " 0.208" ""     " 0.1"   
##   "Mood_Disord_0 = Yes (%)"                    " 0.145" ""     " 0.1"   
##   "Chronic_conditions_0 (mean (SD))"           " 0.475" ""     " 4.6"   
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 0.083" ""     " 0.0"   
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 0.609" ""     " 0.0"   
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 0.446" ""     " 0.0"   
##   "MAT_Normed_0 (mean (SD))"                   " 0.032" ""     " 0.0"   
##   "RVLT_Immediate_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Lang_0 = French (%)"           "<0.001" ""     " 0.0"   
##   "MAT_Lang_0 = French (%)"                    "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "PASE_TOTAL_0 (mean (SD))"                   " 0.694" ""     "18.3"   
##   "PASE_Q1B_0 = 1 (%)"                         " 0.516" ""     " 1.4"   
##   "RSTLS_Sleep_0 = 1 (%)"                      " 0.718" ""     " 0.3"

Females 65+

Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.FemalesOld)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)

##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected after COVID-19"
##   "n"                                          "   827"                           
##   "Age_0 (mean (SD))"                          " 73.52 (5.56)"                    
##   "Sex_0 = F (%)"                              "   827 (100.0) "                  
##   "BMI_0 (mean (SD))"                          " 27.31 (5.19)"                    
##   "Ethnicity_0 = White (%)"                    "   816 ( 98.7) "                  
##   "Relationship_status_0 (%)"                  "   "                              
##   "   Divorced"                                "    98 ( 11.9) "                  
##   "   Married"                                 "   415 ( 50.2) "                  
##   "   Separated"                               "    12 (  1.5) "                  
##   "   Single"                                  "    46 (  5.6) "                  
##   "   Widowed"                                 "   256 ( 31.0) "                  
##   "Education4_0 (%)"                           "   "                              
##   "   College Degree or Higher"                "   444 ( 53.7) "                  
##   "   High School Diploma"                     "   167 ( 20.2) "                  
##   "   Less than High School Diploma"           "   148 ( 17.9) "                  
##   "   Some College"                            "    68 (  8.2) "                  
##   "Income_Level_0 (%)"                         "   "                              
##   "   <$20k"                                   "   261 ( 33.5) "                  
##   "   >$150k"                                  "     6 (  0.8) "                  
##   "   $100-150k"                               "    10 (  1.3) "                  
##   "   $20-50k"                                 "   399 ( 51.2) "                  
##   "   $50-100k"                                "   104 ( 13.3) "                  
##   "Living_status_0 (%)"                        "   "                              
##   "   Apartment/Condo/Townhome"                "   189 ( 22.9) "                  
##   "   Assisted Living"                         "    17 (  2.1) "                  
##   "   House"                                   "   608 ( 73.5) "                  
##   "   Other"                                   "    13 (  1.6) "                  
##   "Alcohol_0 (%)"                              "   "                              
##   "   Non-drinker"                             "   109 ( 14.1) "                  
##   "   Occasional drinker"                      "   186 ( 24.1) "                  
##   "   Regular drinker (at least once a month)" "   477 ( 61.8) "                  
##   "Smoking_Status_0 (%)"                       "   "                              
##   "   Daily Smoker"                            "    33 (  4.0) "                  
##   "   Former Smoker"                           "   459 ( 55.7) "                  
##   "   Never Smoked"                            "   324 ( 39.3) "                  
##   "   Occasional Smoker"                       "     8 (  1.0) "                  
##   "CESD_10_0 (mean (SD))"                      "  5.33 (4.22)"                    
##   "Anxiety_0 = Yes (%)"                        "    54 (  6.5) "                  
##   "Mood_Disord_0 = Yes (%)"                    "    97 ( 11.7) "                  
##   "Chronic_conditions_0 (mean (SD))"           "  4.16 (2.60)"                    
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 10.92 (3.97)"                    
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 11.36 (3.91)"                    
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 10.17 (3.03)"                    
##   "MAT_Normed_0 (mean (SD))"                   "  9.94 (3.71)"                    
##   "RVLT_Immediate_Lang_0 = French (%)"         "   218 ( 26.4) "                  
##   "RVLT_Delayed_Lang_0 = French (%)"           "   218 ( 26.4) "                  
##   "MAT_Lang_0 = French (%)"                    "   218 ( 26.4) "                  
##   "Animal_Fluency_Lang_0 = French (%)"         "   218 ( 26.4) "                  
##   "PASE_TOTAL_0 (mean (SD))"                   "119.62 (54.01)"                   
##   "PASE_Q1B_0 = 1 (%)"                         "   383 ( 47.0) "                  
##   "RSTLS_Sleep_0 = 1 (%)"                      "   262 ( 31.7) "                  
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected before COVID-19"
##   "n"                                          "  1317"                            
##   "Age_0 (mean (SD))"                          " 72.42 (5.62)"                     
##   "Sex_0 = F (%)"                              "  1317 (100.0) "                   
##   "BMI_0 (mean (SD))"                          " 27.00 (5.16)"                     
##   "Ethnicity_0 = White (%)"                    "  1293 ( 98.2) "                   
##   "Relationship_status_0 (%)"                  "  "                                
##   "   Divorced"                                "   193 ( 14.7) "                   
##   "   Married"                                 "   660 ( 50.2) "                   
##   "   Separated"                               "    19 (  1.4) "                   
##   "   Single"                                  "    79 (  6.0) "                   
##   "   Widowed"                                 "   364 ( 27.7) "                   
##   "Education4_0 (%)"                           "  "                                
##   "   College Degree or Higher"                "   975 ( 74.0) "                   
##   "   High School Diploma"                     "   157 ( 11.9) "                   
##   "   Less than High School Diploma"           "   106 (  8.0) "                   
##   "   Some College"                            "    79 (  6.0) "                   
##   "Income_Level_0 (%)"                         "  "                                
##   "   <$20k"                                   "   315 ( 25.7) "                   
##   "   >$150k"                                  "     9 (  0.7) "                   
##   "   $100-150k"                               "    19 (  1.5) "                   
##   "   $20-50k"                                 "   667 ( 54.4) "                   
##   "   $50-100k"                                "   216 ( 17.6) "                   
##   "Living_status_0 (%)"                        "  "                                
##   "   Apartment/Condo/Townhome"                "   272 ( 20.7) "                   
##   "   Assisted Living"                         "    23 (  1.7) "                   
##   "   House"                                   "  1006 ( 76.4) "                   
##   "   Other"                                   "    16 (  1.2) "                   
##   "Alcohol_0 (%)"                              "  "                                
##   "   Non-drinker"                             "   163 ( 13.2) "                   
##   "   Occasional drinker"                      "   269 ( 21.8) "                   
##   "   Regular drinker (at least once a month)" "   801 ( 65.0) "                   
##   "Smoking_Status_0 (%)"                       "  "                                
##   "   Daily Smoker"                            "    52 (  4.0) "                   
##   "   Former Smoker"                           "   763 ( 58.3) "                   
##   "   Never Smoked"                            "   484 ( 37.0) "                   
##   "   Occasional Smoker"                       "    10 (  0.8) "                   
##   "CESD_10_0 (mean (SD))"                      "  5.13 (4.43)"                     
##   "Anxiety_0 = Yes (%)"                        "    90 (  6.8) "                   
##   "Mood_Disord_0 = Yes (%)"                    "   156 ( 11.9) "                   
##   "Chronic_conditions_0 (mean (SD))"           "  3.99 (2.59)"                     
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 11.18 (3.99)"                     
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 11.45 (3.84)"                     
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 10.46 (2.87)"                     
##   "MAT_Normed_0 (mean (SD))"                   "  9.97 (3.60)"                     
##   "RVLT_Immediate_Lang_0 = French (%)"         "   104 (  7.9) "                   
##   "RVLT_Delayed_Lang_0 = French (%)"           "   104 (  7.9) "                   
##   "MAT_Lang_0 = French (%)"                    "   104 (  7.9) "                   
##   "Animal_Fluency_Lang_0 = French (%)"         "   104 (  7.9) "                   
##   "PASE_TOTAL_0 (mean (SD))"                   "115.93 (56.88)"                    
##   "PASE_Q1B_0 = 1 (%)"                         "   549 ( 41.9) "                   
##   "RSTLS_Sleep_0 = 1 (%)"                      "   435 ( 33.1) "                   
##                                               "Stratified by Pandemic"
##  ""                                            "p"      "test" "Missing"
##   "n"                                          ""       ""     "    "   
##   "Age_0 (mean (SD))"                          "<0.001" ""     " 0.0"   
##   "Sex_0 = F (%)"                              "    NA" ""     " 0.0"   
##   "BMI_0 (mean (SD))"                          " 0.184" ""     " 0.7"   
##   "Ethnicity_0 = White (%)"                    " 0.484" ""     " 0.0"   
##   "Relationship_status_0 (%)"                  " 0.283" ""     " 0.1"   
##   "   Divorced"                                ""       ""     "    "   
##   "   Married"                                 ""       ""     "    "   
##   "   Separated"                               ""       ""     "    "   
##   "   Single"                                  ""       ""     "    "   
##   "   Widowed"                                 ""       ""     "    "   
##   "Education4_0 (%)"                           "<0.001" ""     " 0.0"   
##   "   College Degree or Higher"                ""       ""     "    "   
##   "   High School Diploma"                     ""       ""     "    "   
##   "   Less than High School Diploma"           ""       ""     "    "   
##   "   Some College"                            ""       ""     "    "   
##   "Income_Level_0 (%)"                         " 0.002" ""     " 6.4"   
##   "   <$20k"                                   ""       ""     "    "   
##   "   >$150k"                                  ""       ""     "    "   
##   "   $100-150k"                               ""       ""     "    "   
##   "   $20-50k"                                 ""       ""     "    "   
##   "   $50-100k"                                ""       ""     "    "   
##   "Living_status_0 (%)"                        " 0.487" ""     " 0.0"   
##   "   Apartment/Condo/Townhome"                ""       ""     "    "   
##   "   Assisted Living"                         ""       ""     "    "   
##   "   House"                                   ""       ""     "    "   
##   "   Other"                                   ""       ""     "    "   
##   "Alcohol_0 (%)"                              " 0.347" ""     " 6.5"   
##   "   Non-drinker"                             ""       ""     "    "   
##   "   Occasional drinker"                      ""       ""     "    "   
##   "   Regular drinker (at least once a month)" ""       ""     "    "   
##   "Smoking_Status_0 (%)"                       " 0.663" ""     " 0.5"   
##   "   Daily Smoker"                            ""       ""     "    "   
##   "   Former Smoker"                           ""       ""     "    "   
##   "   Never Smoked"                            ""       ""     "    "   
##   "   Occasional Smoker"                       ""       ""     "    "   
##   "CESD_10_0 (mean (SD))"                      " 0.294" ""     " 0.4"   
##   "Anxiety_0 = Yes (%)"                        " 0.855" ""     " 0.1"   
##   "Mood_Disord_0 = Yes (%)"                    " 0.985" ""     " 0.0"   
##   "Chronic_conditions_0 (mean (SD))"           " 0.166" ""     " 5.5"   
##   "RVLT_Immediate_Normed_0 (mean (SD))"        " 0.140" ""     " 0.0"   
##   "RVLT_Delayed_Normed_0 (mean (SD))"          " 0.589" ""     " 0.0"   
##   "Animal_Fluency_Normed_0 (mean (SD))"        " 0.026" ""     " 0.0"   
##   "MAT_Normed_0 (mean (SD))"                   " 0.837" ""     " 0.0"   
##   "RVLT_Immediate_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "RVLT_Delayed_Lang_0 = French (%)"           "<0.001" ""     " 0.0"   
##   "MAT_Lang_0 = French (%)"                    "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Lang_0 = French (%)"         "<0.001" ""     " 0.0"   
##   "PASE_TOTAL_0 (mean (SD))"                   " 0.193" ""     "23.2"   
##   "PASE_Q1B_0 = 1 (%)"                         " 0.025" ""     " 0.9"   
##   "RSTLS_Sleep_0 = 1 (%)"                      " 0.544" ""     " 0.1"

4) Linear mixed model set-up

Linear mixed model set-up

Tracking.data.short<-Tracking.Adjusted_Final[c(1,4:12,14,15,18:21,146,147,98,115,132,102,119,136,106,123,140,110,127,144,13,44,75,22,53,84,23,54,85)]

Tracking.data.short2<-rename(Tracking.data.short, c("Age"="Age_0","Sex"="Sex_0","Ethnicity"="Ethnicity_0","Relationshipstatus"="Relationship_status_0",
                             "Education"="Education4_0", "IncomeLevel"="Income_Level_0", "Livingstatus"="Living_status_0", 
                             "Alcohol"="Alcohol_0", "SmokingStatus"="Smoking_Status_0","Anxiety"="Anxiety_0","MoodDisord"="Mood_Disord_0",
                             "Chronicconditions"="Chronic_conditions_0", "BMI"="BMI_0","PASE_Sit_0"="PASE_Q1B_0","PASE_Sit_1"="PASE_Q1B_1","PASE_Sit_2"="PASE_Q1B_2"))

Tracking.data.short2$PASE_TOTALbaseline <- Tracking.data.short2$PASE_TOTAL_0

Tracking.data.short3<-Tracking.data.short2[c(1:18,40,19:39)]
Tracking.data.short4<-Tracking.data.short3[c(1:18,20,23,26,29,32,35,38,21:22,24:25,27:28,30:31,33:34,36:37,39:40)]
colnames(Tracking.data.short3) <- (gsub("_2",".3",colnames(Tracking.data.short3)))
colnames(Tracking.data.short3) <- (gsub("_1",".2",colnames(Tracking.data.short3)))
colnames(Tracking.data.short3) <- (gsub("_0",".1",colnames(Tracking.data.short3)))

colnames(Tracking.data.short4) <- (gsub("_2",".2",colnames(Tracking.data.short4)))
colnames(Tracking.data.short4) <- (gsub("_1",".1",colnames(Tracking.data.short4)))
colnames(Tracking.data.short4) <- (gsub("_0","baseline",colnames(Tracking.data.short4)))


Tracking.data_long <- reshape(as.data.frame(Tracking.data.short3),idvar="ID",varying=20:40,direction="long",sep=".") #reshape data into long format (3 timepoints)
Tracking.data_long_2 <- reshape(as.data.frame(Tracking.data.short4),idvar="ID",varying=26:39,direction="long",sep=".") #reshape data into long format (3 timepoints)

Indexed time as a categorical factor

#Treat time as a fixed effect
Tracking.data_long$timefactor<-as.factor(Tracking.data_long$time)
Tracking.data_long_2$timefactor<-as.factor(Tracking.data_long_2$time)

5) Base model: Time x Pandemic effects on cognition, physical activity, and sleep duration (not adjusting for baseline)

Increase the data matrix to support the models

emm_options(rg.limit = 150000)
emm_options(opt.digits = FALSE)

Contrast statements

#Contrast 1: Group differences from baseline to FU1
c1=matrix(c(0,1,0,-1,0,0))
c2=matrix(c(1,0,-1,0,0,0))
c1st=c1 - c2

#Contrast 2: Group differences from FU1 to FU2
c1=matrix(c(0,0,0,1,0,-1))
c2=matrix(c(0,0,1,0,-1,0))
c2nd = c1 - c2

#Contrast 3: Group differences from baseline to FU2
c1=matrix(c(0,1,0,0,0,-1))
c2=matrix(c(1,0,0,0,-1,0))
c3rd = c1 - c2

For each of the cognitive models, we used the normalized scores as the dependent variable. Baseline is not adjusted and no covariates were included.

5.1) RVLT Immediate Recall

5.1.1) Model

track.modelRVLT_imm_3<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + (1|ID), data= Tracking.data_long)
summary(track.modelRVLT_imm_3)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Immediate_Normed ~ timefactor * Pandemic + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 178154.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6062 -0.5765 -0.0440  0.5241  5.0473 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.477    2.340   
##  Residual             8.532    2.921   
## Number of obs: 33347, groups:  ID, 11355
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             1.013e+01  5.200e-02
## timefactor2                                             6.845e-01  5.784e-02
## timefactor3                                             9.738e-01  5.845e-02
## PandemicFU2 data collected before COVID-19              5.726e-01  7.052e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -2.406e-01  7.846e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -3.149e-01  7.891e-02
##                                                                df t value
## (Intercept)                                             2.566e+04 194.776
## timefactor2                                             2.209e+04  11.834
## timefactor3                                             2.222e+04  16.662
## PandemicFU2 data collected before COVID-19              2.566e+04   8.119
## timefactor2:PandemicFU2 data collected before COVID-19  2.209e+04  -3.067
## timefactor3:PandemicFU2 data collected before COVID-19  2.217e+04  -3.991
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## timefactor3                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             4.90e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19  0.00217 ** 
## timefactor3:PandemicFU2 data collected before COVID-19 6.59e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) tmfct2 tmfct3 PFdcbC t2dcbC
## timefactor2 -0.548                            
## timefactor3 -0.542  0.487                     
## PFU2dcbCOVI -0.737  0.404  0.400              
## t2:PFU2dcbC  0.404 -0.737 -0.359 -0.547       
## t3:PFU2dcbC  0.401 -0.361 -0.741 -0.544  0.490

5.1.2) Estimated marginal means

lsmeans(track.modelRVLT_imm_3, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.12814 0.05199884 Inf  10.02623  10.23006
##  FU2 data collected before COVID-19 10.70071 0.04763401 Inf  10.60735  10.79407
## 
## timefactor = 2:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.81261 0.05249420 Inf  10.70973  10.91550
##  FU2 data collected before COVID-19 11.14457 0.04811768 Inf  11.05026  11.23887
## 
## timefactor = 3:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.10199 0.05316238 Inf  10.99779  11.20619
##  FU2 data collected before COVID-19 11.35962 0.04811768 Inf  11.26531  11.45393
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(track.modelRVLT_imm_3, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.5725642 0.07051863 Inf  -8.119  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.3319512 0.07121062 Inf  -4.662  <.0001
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.2576324 0.07170460 Inf  -3.593  0.0003
## 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(track.modelRVLT_imm_3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.5725642 0.07051863 Inf -0.7107782 -0.4343503
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.3319512 0.07121062 Inf -0.4715214 -0.1923809
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.2576324 0.07170460 Inf -0.3981709 -0.1170940
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

5.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_3 <- summary(lsmeans(track.modelRVLT_imm_3, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTimmediate_lsmeans_3$Time<-NA
RVLTimmediate_lsmeans_3$Time[RVLTimmediate_lsmeans_3$timefactor==1]<-"Baseline"
RVLTimmediate_lsmeans_3$Time[RVLTimmediate_lsmeans_3$timefactor==2]<-"Follow-up 1"
RVLTimmediate_lsmeans_3$Time[RVLTimmediate_lsmeans_3$timefactor==3]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_3, aes(x = Time, y = lsmean, group = Pandemic)) +
  geom_line(aes(linetype = Pandemic)) +
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

5.1.4) Planned contrasts

lsmeans.RVLTImm1 <- lsmeans(track.modelRVLT_imm_3, ~ Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.RVLTImm1,list(c1st,c2nd,c3rd),by=NULL)

##  contrast                                            estimate         SE  df
##  structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.24061305 0.07845730 Inf
##  structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) 0.07431874 0.07949815 Inf
##  structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) 0.31493179 0.07890593 Inf
##  z.ratio p.value
##    3.067  0.0022
##    0.935  0.3499
##    3.991  0.0001
## 
## Degrees-of-freedom method: asymptotic

5.2) RVLT Delayed Recall

5.2.1) Model

modelRVLT_del_3<- lmer(RVLT_Delayed_Normed ~ timefactor*Pandemic + (1|ID), data= Tracking.data_long)
summary(modelRVLT_del_3)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 175144.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8201 -0.5593 -0.0388  0.5068  5.0673 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.794    2.407   
##  Residual             7.766    2.787   
## Number of obs: 33150, groups:  ID, 11355
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             1.045e+01  5.116e-02
## timefactor2                                             5.168e-01  5.538e-02
## timefactor3                                             1.087e+00  5.598e-02
## PandemicFU2 data collected before COVID-19              4.873e-01  6.938e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -5.271e-02  7.510e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -3.268e-01  7.553e-02
##                                                                df t value
## (Intercept)                                             2.447e+04 204.162
## timefactor2                                             2.195e+04   9.331
## timefactor3                                             2.207e+04  19.423
## PandemicFU2 data collected before COVID-19              2.447e+04   7.023
## timefactor2:PandemicFU2 data collected before COVID-19  2.195e+04  -0.702
## timefactor3:PandemicFU2 data collected before COVID-19  2.201e+04  -4.327
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## timefactor3                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             2.23e-12 ***
## timefactor2:PandemicFU2 data collected before COVID-19    0.483    
## timefactor3:PandemicFU2 data collected before COVID-19 1.52e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) tmfct2 tmfct3 PFdcbC t2dcbC
## timefactor2 -0.529                            
## timefactor3 -0.523  0.484                     
## PFU2dcbCOVI -0.737  0.390  0.386              
## t2:PFU2dcbC  0.390 -0.737 -0.357 -0.529       
## t3:PFU2dcbC  0.388 -0.359 -0.741 -0.526  0.487

5.2.2) Estimated marginal means

lsmeans(modelRVLT_del_3, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.44504 0.05116045 Inf  10.34477  10.54531
##  FU2 data collected before COVID-19 10.93232 0.04686600 Inf  10.84046  11.02417
## 
## timefactor = 2:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.96183 0.05183315 Inf  10.86024  11.06342
##  FU2 data collected before COVID-19 11.39640 0.04746859 Inf  11.30337  11.48944
## 
## timefactor = 3:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.53230 0.05246753 Inf  11.42947  11.63514
##  FU2 data collected before COVID-19 11.69276 0.04745071 Inf  11.59976  11.78576
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_del_3, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.4872765 0.06938165 Inf  -7.023  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.4345710 0.07028473 Inf  -6.183  <.0001
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1604577 0.07074186 Inf  -2.268  0.0233
## 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_del_3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.4872765 0.06938165 Inf -0.6232620 -0.3512909
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.4345710 0.07028473 Inf -0.5723265 -0.2968154
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.1604577 0.07074186 Inf -0.2991092 -0.0218062
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

5.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_3 <- summary(lsmeans(modelRVLT_del_3, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTdelayed_lsmeans_3$Time<-NA
RVLTdelayed_lsmeans_3$Time[RVLTdelayed_lsmeans_3$timefactor==1]<-"Baseline"
RVLTdelayed_lsmeans_3$Time[RVLTdelayed_lsmeans_3$timefactor==2]<-"Follow-up 1"
RVLTdelayed_lsmeans_3$Time[RVLTdelayed_lsmeans_3$timefactor==3]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_3, aes(x = Time, y = lsmean, group = Pandemic)) +
  geom_line(aes(linetype = Pandemic)) +
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

5.2.4) Planned contrasts

lsmeans.RVLTDel1 <- lsmeans(modelRVLT_del_3, ~ Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.RVLTDel1,list(c1st,c2nd,c3rd),by=NULL)

##  contrast                                           estimate         SE  df
##  structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.0527055 0.07509956 Inf
##  structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) 0.2741133 0.07631383 Inf
##  structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) 0.3268188 0.07552756 Inf
##  z.ratio p.value
##    0.702  0.4828
##    3.592  0.0003
##    4.327  <.0001
## 
## Degrees-of-freedom method: asymptotic

5.3) Mental Alteration Test

5.3.1) Model

modelMAT_3<- lmer(MAT_Normed~ timefactor*Pandemic + (1|ID), data= Tracking.data_long)
summary(modelMAT_3)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 167568.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0453 -0.4679 -0.0205  0.4174  4.9277 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.319    2.306   
##  Residual             7.729    2.780   
## Number of obs: 31829, groups:  ID, 11355
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             9.820e+00  5.018e-02
## timefactor2                                             1.179e+00  5.656e-02
## timefactor3                                            -2.344e-01  5.702e-02
## PandemicFU2 data collected before COVID-19              2.448e-01  6.806e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -5.428e-01  7.657e-02
## timefactor3:PandemicFU2 data collected before COVID-19  1.161e-02  7.691e-02
##                                                                df t value
## (Intercept)                                             2.428e+04 195.679
## timefactor2                                             2.092e+04  20.843
## timefactor3                                             2.101e+04  -4.111
## PandemicFU2 data collected before COVID-19              2.428e+04   3.597
## timefactor2:PandemicFU2 data collected before COVID-19  2.089e+04  -7.089
## timefactor3:PandemicFU2 data collected before COVID-19  2.094e+04   0.151
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## timefactor3                                            3.96e-05 ***
## PandemicFU2 data collected before COVID-19             0.000322 ***
## timefactor2:PandemicFU2 data collected before COVID-19 1.39e-12 ***
## timefactor3:PandemicFU2 data collected before COVID-19 0.880023    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) tmfct2 tmfct3 PFdcbC t2dcbC
## timefactor2 -0.526                            
## timefactor3 -0.521  0.465                     
## PFU2dcbCOVI -0.737  0.388  0.384              
## t2:PFU2dcbC  0.388 -0.739 -0.343 -0.527       
## t3:PFU2dcbC  0.387 -0.345 -0.741 -0.524  0.468

5.3.2) Estimated marginal means

lsmeans(modelMAT_3, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                              lsmean         SE  df asymp.LCL
##  FU2 data collected after COVID-19   9.819871 0.05018368 Inf  9.721513
##  FU2 data collected before COVID-19 10.064693 0.04597121 Inf  9.974591
##  asymp.UCL
##   9.918229
##  10.154795
## 
## timefactor = 2:
##  Pandemic                              lsmean         SE  df asymp.LCL
##  FU2 data collected after COVID-19  10.998711 0.05228331 Inf 10.896238
##  FU2 data collected before COVID-19 10.700711 0.04767891 Inf 10.607262
##  asymp.UCL
##  11.101185
##  10.794160
## 
## timefactor = 3:
##  Pandemic                              lsmean         SE  df asymp.LCL
##  FU2 data collected after COVID-19   9.585467 0.05278333 Inf  9.482013
##  FU2 data collected before COVID-19  9.841897 0.04767230 Inf  9.748461
##  asymp.UCL
##   9.688920
##   9.935333
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelMAT_3, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.2448220 0.06805699 Inf  -3.597  0.0003
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##   0.2980000 0.07075891 Inf   4.211  <.0001
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.2564302 0.07112474 Inf  -3.605  0.0003
## 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelMAT_3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.2448220 0.06805699 Inf -0.3782112 -0.1114327
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##   0.2980000 0.07075891 Inf  0.1593151  0.4366849
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.2564302 0.07112474 Inf -0.3958321 -0.1170283
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

5.3.3) Graph of estimated marginal means

MAT_lsmeans_3 <- summary(lsmeans(modelMAT_3, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.

MAT_lsmeans_3$Time<-NA
MAT_lsmeans_3$Time[MAT_lsmeans_3$timefactor==1]<-"Baseline"
MAT_lsmeans_3$Time[MAT_lsmeans_3$timefactor==2]<-"Follow-up 1"
MAT_lsmeans_3$Time[MAT_lsmeans_3$timefactor==3]<-"Follow-up 2"
ggplot(MAT_lsmeans_3, aes(x = Time, y = lsmean, group = Pandemic)) +
  geom_line(aes(linetype = Pandemic)) +
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

5.3.4) Planned contrasts

lsmeans.MAT1 <- lsmeans(modelMAT_3, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.MAT1,list(c1st,c2nd,c3rd),by=NULL)

##  contrast                                            estimate         SE  df
##  structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L))  0.5428220 0.07656705 Inf
##  structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) -0.5544302 0.07917868 Inf
##  structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) -0.0116082 0.07690526 Inf
##  z.ratio p.value
##    7.089  <.0001
##   -7.002  <.0001
##   -0.151  0.8800
## 
## Degrees-of-freedom method: asymptotic

5.4) Animal Fluency

5.4.1) Model

modelAnimals_3<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + (1|ID), data= Tracking.data_long)
summary(modelAnimals_3)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Animal_Fluency_Normed ~ timefactor * Pandemic + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 156896.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.3392 -0.5628 -0.0186  0.5482  4.5591 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.041    2.245   
##  Residual             3.656    1.912   
## Number of obs: 33494, groups:  ID, 11355
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             1.019e+01  4.097e-02
## timefactor2                                            -8.038e-02  3.781e-02
## timefactor3                                            -1.899e-02  3.826e-02
## PandemicFU2 data collected before COVID-19              1.883e-01  5.556e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -2.083e-02  5.126e-02
## timefactor3:PandemicFU2 data collected before COVID-19  1.048e-02  5.160e-02
##                                                                df t value
## (Intercept)                                             2.020e+04 248.636
## timefactor2                                             2.220e+04  -2.126
## timefactor3                                             2.229e+04  -0.496
## PandemicFU2 data collected before COVID-19              2.020e+04   3.388
## timefactor2:PandemicFU2 data collected before COVID-19  2.219e+04  -0.406
## timefactor3:PandemicFU2 data collected before COVID-19  2.225e+04   0.203
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            0.033500 *  
## timefactor3                                            0.619625    
## PandemicFU2 data collected before COVID-19             0.000705 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.684453    
## timefactor3:PandemicFU2 data collected before COVID-19 0.839056    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) tmfct2 tmfct3 PFdcbC t2dcbC
## timefactor2 -0.456                            
## timefactor3 -0.450  0.488                     
## PFU2dcbCOVI -0.737  0.336  0.332              
## t2:PFU2dcbC  0.336 -0.738 -0.360 -0.456       
## t3:PFU2dcbC  0.334 -0.362 -0.741 -0.453  0.491

5.4.2) Estimated marginal means

lsmeans(modelAnimals_3, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.18698 0.04097150 Inf  10.10667  10.26728
##  FU2 data collected before COVID-19 10.37523 0.03753232 Inf  10.30167  10.44879
## 
## timefactor = 2:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.10659 0.04119001 Inf  10.02586  10.18732
##  FU2 data collected before COVID-19 10.27402 0.03771754 Inf  10.20009  10.34794
## 
## timefactor = 3:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.16798 0.04161010 Inf  10.08643  10.24953
##  FU2 data collected before COVID-19 10.36672 0.03772654 Inf  10.29278  10.44066
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelAnimals_3, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1882578 0.05556383 Inf  -3.388  0.0007
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1674258 0.05585006 Inf  -2.998  0.0027
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1987389 0.05616665 Inf  -3.538  0.0004
## 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelAnimals_3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.1882578 0.05556383 Inf -0.2971609 -0.07935470
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.1674258 0.05585006 Inf -0.2768899 -0.05796166
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.1987389 0.05616665 Inf -0.3088235 -0.08865428
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

5.4.3) Graph of estimated marginal means

Animals_lsmeans_3 <- summary(lsmeans(modelAnimals_3, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.

Animals_lsmeans_3$Time<-NA
Animals_lsmeans_3$Time[Animals_lsmeans_3$timefactor==1]<-"Baseline"
Animals_lsmeans_3$Time[Animals_lsmeans_3$timefactor==2]<-"Follow-up 1"
Animals_lsmeans_3$Time[Animals_lsmeans_3$timefactor==3]<-"Follow-up 2"
ggplot(Animals_lsmeans_3, aes(x = Time, y = lsmean, group = Pandemic)) +
  geom_line(aes(linetype = Pandemic)) +
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

5.4.4) Planned contrasts

lsmeans.Animals1 <- lsmeans(modelAnimals_3, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.Animals1,list(c1st,c2nd,c3rd),by=NULL)

##  contrast                                             estimate         SE  df
##  structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L))  0.02083203 0.05125999 Inf
##  structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) -0.03131311 0.05189247 Inf
##  structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) -0.01048108 0.05160474 Inf
##  z.ratio p.value
##    0.406  0.6844
##   -0.603  0.5462
##   -0.203  0.8391
## 
## Degrees-of-freedom method: asymptotic

6) Base model: Time x Pandemic effects on cognition, physical activity, and sleep duration (adjusted for baseline)

Contrast statements

#Contrast 1: Group differences from baseline to FU1
c1=matrix(c(0,1,0,-1))
c2=matrix(c(1,0,-1,0))
c4th=c1 - c2

All cognitive models used normalized scores as the dependent variable. Baseline cognitive performance was included as a covariate. No other covariates were included.

6.1) RVLT Immediate Recall

6.1.1) Model

modelRVLT_imm_adj<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + RVLT_Immediate_Normedbaseline + (1|ID), data= Tracking.data_long_2)
summary(modelRVLT_imm_adj)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## RVLT_Immediate_Normed ~ timefactor * Pandemic + RVLT_Immediate_Normedbaseline +  
##     (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 115453.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5846 -0.5593 -0.0308  0.5271  3.9457 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.40     2.098   
##  Residual             7.56     2.750   
## Number of obs: 21992, groups:  ID, 11325
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             7.224e+00  8.713e-02
## timefactor2                                             2.885e-01  5.551e-02
## PandemicFU2 data collected before COVID-19              1.291e-01  6.600e-02
## RVLT_Immediate_Normedbaseline                           3.542e-01  7.141e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -7.246e-02  7.493e-02
##                                                                df t value
## (Intercept)                                             1.368e+04  82.911
## timefactor2                                             1.109e+04   5.197
## PandemicFU2 data collected before COVID-19              1.958e+04   1.956
## RVLT_Immediate_Normedbaseline                           1.127e+04  49.598
## timefactor2:PandemicFU2 data collected before COVID-19  1.102e+04  -0.967
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            2.06e-07 ***
## PandemicFU2 data collected before COVID-19               0.0505 .  
## RVLT_Immediate_Normedbaseline                           < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19   0.3336    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) tmfct2 PFdcbC RVLT_I
## timefactor2 -0.312                     
## PFU2dcbCOVI -0.359  0.411              
## RVLT_Immd_N -0.830  0.001 -0.062       
## t2:PFU2dcbC  0.232 -0.741 -0.561 -0.001

6.1.2) Estimated marginal means

lsmeans(modelRVLT_imm_adj, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.92317 0.04860791 Inf  10.82790  11.01844
##  FU2 data collected before COVID-19 11.05226 0.04454865 Inf  10.96495  11.13957
## 
## timefactor = 2:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.21165 0.04929225 Inf  11.11504  11.30826
##  FU2 data collected before COVID-19 11.26829 0.04455099 Inf  11.18097  11.35561
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_imm_adj, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.12909271 0.06599630 Inf  -1.956  0.0505
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.05663759 0.06650667 Inf  -0.852  0.3944
## 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_imm_adj, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.12909271 0.06599630 Inf -0.2584431 0.00025766
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.05663759 0.06650667 Inf -0.1869883 0.07371308
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

6.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_adj <- summary(lsmeans(modelRVLT_imm_adj, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTimmediate_lsmeans_adj$Time<-NA
RVLTimmediate_lsmeans_adj$Time[RVLTimmediate_lsmeans_adj$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj$Time[RVLTimmediate_lsmeans_adj$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) +
  labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

6.1.4) Planned contrasts

lsmeans.RVLTImm2 <- lsmeans(modelRVLT_imm_adj, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.RVLTImm2,list(c4th),by=NULL)

##  contrast                                      estimate         SE  df z.ratio
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.07245512 0.07493437 Inf   0.967
##  p.value
##   0.3336
## 
## Degrees-of-freedom method: asymptotic

6.2) RVLT Delayed Recall

6.2.1) Model

modelRVLT_del_adj<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic + RVLT_Delayed_Normedbaseline + (1|ID), data= Tracking.data_long_2)
summary(modelRVLT_del_adj)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## RVLT_Delayed_Normed ~ timefactor * Pandemic + RVLT_Delayed_Normedbaseline +  
##     (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 113063.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8852 -0.5489 -0.0326  0.5078  4.1922 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.283    2.069   
##  Residual             7.004    2.646   
## Number of obs: 21795, groups:  ID, 11307
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             6.795e+00  8.862e-02
## timefactor2                                             5.700e-01  5.382e-02
## PandemicFU2 data collected before COVID-19              2.406e-01  6.434e-02
## RVLT_Delayed_Normedbaseline                             3.988e-01  7.162e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -2.737e-01  7.258e-02
##                                                                df t value
## (Intercept)                                             1.341e+04  76.674
## timefactor2                                             1.101e+04  10.591
## PandemicFU2 data collected before COVID-19              1.937e+04   3.740
## RVLT_Delayed_Normedbaseline                             1.123e+04  55.680
## timefactor2:PandemicFU2 data collected before COVID-19  1.093e+04  -3.771
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             0.000185 ***
## RVLT_Delayed_Normedbaseline                             < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.000164 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) tmfct2 PFdcbC RVLT_D
## timefactor2 -0.298                     
## PFU2dcbCOVI -0.348  0.409              
## RVLT_Dlyd_N -0.845  0.001 -0.054       
## t2:PFU2dcbC  0.221 -0.741 -0.557  0.000

6.2.2) Estimated marginal means

lsmeans(modelRVLT_del_adj, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.07067 0.04742186 Inf  10.97773  11.16362
##  FU2 data collected before COVID-19 11.31130 0.04342153 Inf  11.22620  11.39641
## 
## timefactor = 2:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.64071 0.04808827 Inf  11.54646  11.73496
##  FU2 data collected before COVID-19 11.60764 0.04340093 Inf  11.52258  11.69271
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_del_adj, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.24062820 0.06434465 Inf  -3.740  0.0002
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##   0.03306667 0.06482383 Inf   0.510  0.6100
## 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_del_adj, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.24062820 0.06434465 Inf -0.3667414 -0.1145150
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL  asymp.UCL
##   0.03306667 0.06482383 Inf -0.0939857  0.1601191
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

6.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_adj <- summary(lsmeans(modelRVLT_del_adj, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTdelayed_lsmeans_adj$Time<-NA
RVLTdelayed_lsmeans_adj$Time[RVLTdelayed_lsmeans_adj$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj$Time[RVLTdelayed_lsmeans_adj$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) +
  labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

6.2.4) Planned contrasts

lsmeans.RVLTDel2 <- lsmeans(modelRVLT_del_adj, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.RVLTDel2,list(c4th),by=NULL)

##  contrast                                     estimate        SE  df z.ratio
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.2736949 0.0725854 Inf   3.771
##  p.value
##   0.0002
## 
## Degrees-of-freedom method: asymptotic

6.3) Mental Alteration Test

6.3.1) Model

modelMAT_adj<- lmer(MAT_Normed~ timefactor*Pandemic + MAT_Normedbaseline + (1|ID), data= Tracking.data_long_2)
summary(modelMAT_adj)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + MAT_Normedbaseline + (1 |  
##     ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 107052.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3284 -0.5035 -0.0867  0.3301  4.9371 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.735    1.654   
##  Residual             8.485    2.913   
## Number of obs: 20474, groups:  ID, 11171
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             6.555e+00  8.903e-02
## timefactor2                                            -1.416e+00  6.142e-02
## PandemicFU2 data collected before COVID-19             -4.083e-01  6.619e-02
## MAT_Normedbaseline                                      4.523e-01  7.521e-03
## timefactor2:PandemicFU2 data collected before COVID-19  5.584e-01  8.278e-02
##                                                                df t value
## (Intercept)                                             1.371e+04  73.634
## timefactor2                                             1.053e+04 -23.048
## PandemicFU2 data collected before COVID-19              1.972e+04  -6.169
## MAT_Normedbaseline                                      1.096e+04  60.139
## timefactor2:PandemicFU2 data collected before COVID-19  1.044e+04   6.746
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             6.99e-10 ***
## MAT_Normedbaseline                                      < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 1.60e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) tmfct2 PFdcbC MAT_Nr
## timefactor2 -0.339                     
## PFU2dcbCOVI -0.385  0.457              
## MAT_Nrmdbsl -0.836 -0.002 -0.026       
## t2:PFU2dcbC  0.251 -0.742 -0.620  0.001

6.3.2) Estimated marginal means

lsmeans(modelMAT_adj, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                              lsmean         SE  df asymp.LCL
##  FU2 data collected after COVID-19  11.091853 0.04892068 Inf 10.995971
##  FU2 data collected before COVID-19 10.683509 0.04456865 Inf 10.596156
##  asymp.UCL
##  11.187736
##  10.770862
## 
## timefactor = 2:
##  Pandemic                              lsmean         SE  df asymp.LCL
##  FU2 data collected after COVID-19   9.676182 0.04948387 Inf  9.579195
##  FU2 data collected before COVID-19  9.826236 0.04456114 Inf  9.738898
##  asymp.UCL
##   9.773168
##   9.913574
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelMAT_adj, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##   0.4083442 0.06618888 Inf   6.169  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1500545 0.06660025 Inf  -2.253  0.0243
## 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelMAT_adj, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##   0.4083442 0.06618888 Inf  0.2786164  0.5380720
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.1500545 0.06660025 Inf -0.2805886 -0.0195204
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

6.3.3) Graph of estimated marginal means

MAT_lsmeans_adj <- summary(lsmeans(modelMAT_adj, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.

MAT_lsmeans_adj$Time<-NA
MAT_lsmeans_adj$Time[MAT_lsmeans_adj$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj$Time[MAT_lsmeans_adj$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) +
  labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

6.3.4) Planned contrasts

lsmeans.MAT2 <- lsmeans(modelMAT_adj, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.MAT2,list(c4th),by=NULL)

##  contrast                                      estimate         SE  df z.ratio
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.5583987 0.08277795 Inf  -6.746
##  p.value
##   <.0001
## 
## Degrees-of-freedom method: asymptotic

6.4) Animal Fluency

6.4.1) Model

modelAnimals_adj<- lmer(Animal_Fluency_Normed~ timefactor*Pandemic + Animal_Fluency_Normedbaseline + (1|ID), data= Tracking.data_long_2)
summary(modelAnimals_adj)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## Animal_Fluency_Normed ~ timefactor * Pandemic + Animal_Fluency_Normedbaseline +  
##     (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 98964.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8744 -0.5507 -0.0246  0.5316  4.6145 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.453    1.566   
##  Residual             3.204    1.790   
## Number of obs: 22139, groups:  ID, 11334
## 
## Fixed effects:
##                                                         Estimate Std. Error
## (Intercept)                                            4.710e+00  7.132e-02
## timefactor2                                            6.205e-02  3.606e-02
## PandemicFU2 data collected before COVID-19             6.775e-02  4.516e-02
## Animal_Fluency_Normedbaseline                          5.297e-01  6.190e-03
## timefactor2:PandemicFU2 data collected before COVID-19 3.004e-02  4.862e-02
##                                                               df t value
## (Intercept)                                            1.274e+04  66.044
## timefactor2                                            1.113e+04   1.720
## PandemicFU2 data collected before COVID-19             1.885e+04   1.500
## Animal_Fluency_Normedbaseline                          1.126e+04  85.574
## timefactor2:PandemicFU2 data collected before COVID-19 1.106e+04   0.618
##                                                        Pr(>|t|)    
## (Intercept)                                              <2e-16 ***
## timefactor2                                              0.0854 .  
## PandemicFU2 data collected before COVID-19               0.1335    
## Animal_Fluency_Normedbaseline                            <2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19   0.5367    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) tmfct2 PFdcbC An_F_N
## timefactor2 -0.245                     
## PFU2dcbCOVI -0.321  0.390              
## Anml_Flnc_N -0.884 -0.001 -0.026       
## t2:PFU2dcbC  0.182 -0.742 -0.531  0.001

6.4.2) Estimated marginal means

lsmeans(modelAnimals_adj, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.16662 0.03330039 Inf  10.10136  10.23189
##  FU2 data collected before COVID-19 10.23438 0.03048521 Inf  10.17463  10.29413
## 
## timefactor = 2:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.22867 0.03377601 Inf  10.16247  10.29487
##  FU2 data collected before COVID-19 10.32647 0.03049575 Inf  10.26670  10.38624
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelAnimals_adj, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.06775267 0.04515468 Inf  -1.500  0.1335
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.09779421 0.04551335 Inf  -2.149  0.0317
## 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelAnimals_adj, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL    asymp.UCL
##  -0.06775267 0.04515468 Inf -0.1562542  0.020748877
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL    asymp.UCL
##  -0.09779421 0.04551335 Inf -0.1869987 -0.008589689
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

6.4.3) Graph of estimated marginal means

Animals_lsmeans_adj <- summary(lsmeans(modelAnimals_adj, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.

Animals_lsmeans_adj$Time<-NA
Animals_lsmeans_adj$Time[Animals_lsmeans_adj$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj$Time[Animals_lsmeans_adj$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) +
  labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

6.4.4) Planned contrasts

lsmeans.Animals2 <- lsmeans(modelAnimals_adj, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.Animals2,list(c4th),by=NULL)

##  contrast                                       estimate        SE  df z.ratio
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.03004154 0.0486195 Inf  -0.618
##  p.value
##   0.5366
## 
## Degrees-of-freedom method: asymptotic

7) Simple Adjusted Model: Time x Pandemic effects on cognition and physical activity (not adjusting for baseline)

All cognitive models used normalized scores. Each model is adjusted for age, sex, education level, ethnicity, and income level.

emm_options(rg.limit = 150000)

7.1) RVLT Immediate Recall

7.1.1) Model

modelRVLT_imm_7<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel +
                         (1|ID), data= Tracking.data_long)
summary(modelRVLT_imm_7)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## RVLT_Immediate_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 171754.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6061 -0.5778 -0.0456  0.5216  5.0392 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.296    2.301   
##  Residual             8.493    2.914   
## Number of obs: 32211, groups:  ID, 10961
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             9.050e+00  2.499e-01
## timefactor2                                             6.938e-01  5.859e-02
## timefactor3                                             1.005e+00  5.921e-02
## PandemicFU2 data collected before COVID-19              5.404e-01  7.214e-02
## Age                                                     7.354e-04  2.869e-03
## SexM                                                   -6.536e-01  5.846e-02
## EducationHigh School Diploma                            1.772e-01  8.490e-02
## EducationLess than High School Diploma                  6.349e-01  1.166e-01
## EducationSome College                                   2.389e-01  1.068e-01
## EthnicityWhite                                          8.362e-01  1.679e-01
## IncomeLevel>$150k                                       1.198e+00  1.611e-01
## IncomeLevel$100-150k                                    9.262e-01  1.274e-01
## IncomeLevel$20-50k                                      2.562e-01  8.253e-02
## IncomeLevel$50-100k                                     7.624e-01  8.893e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -2.254e-01  7.961e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -3.217e-01  8.007e-02
##                                                                df t value
## (Intercept)                                             1.130e+04  36.207
## timefactor2                                             2.134e+04  11.842
## timefactor3                                             2.147e+04  16.980
## PandemicFU2 data collected before COVID-19              2.449e+04   7.491
## Age                                                     1.091e+04   0.256
## SexM                                                    1.091e+04 -11.179
## EducationHigh School Diploma                            1.092e+04   2.087
## EducationLess than High School Diploma                  1.099e+04   5.444
## EducationSome College                                   1.087e+04   2.236
## EthnicityWhite                                          1.090e+04   4.981
## IncomeLevel>$150k                                       1.092e+04   7.435
## IncomeLevel$100-150k                                    1.088e+04   7.271
## IncomeLevel$20-50k                                      1.092e+04   3.104
## IncomeLevel$50-100k                                     1.091e+04   8.574
## timefactor2:PandemicFU2 data collected before COVID-19  2.134e+04  -2.831
## timefactor3:PandemicFU2 data collected before COVID-19  2.141e+04  -4.017
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## timefactor3                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             7.08e-14 ***
## Age                                                     0.79771    
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                            0.03692 *  
## EducationLess than High School Diploma                 5.32e-08 ***
## EducationSome College                                   0.02538 *  
## EthnicityWhite                                         6.42e-07 ***
## IncomeLevel>$150k                                      1.12e-13 ***
## IncomeLevel$100-150k                                   3.82e-13 ***
## IncomeLevel$20-50k                                      0.00191 ** 
## IncomeLevel$50-100k                                     < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19  0.00465 ** 
## timefactor3:PandemicFU2 data collected before COVID-19 5.91e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

7.1.2) Estimated marginal means

lsmeans(modelRVLT_imm_7, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.07777 0.1047309 Inf  9.872503  10.28304
##  FU2 data collected before COVID-19 10.61817 0.1051213 Inf 10.412134  10.82420
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.77155 0.1049628 Inf 10.565822  10.97727
##  FU2 data collected before COVID-19 11.08658 0.1053792 Inf 10.880041  11.29312
## 
## timefactor = 3:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.08318 0.1053072 Inf 10.876779  11.28957
##  FU2 data collected before COVID-19 11.30192 0.1053620 Inf 11.095410  11.50842
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_imm_7, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.5403959 0.07214149 Inf  -7.491  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.3150343 0.07279314 Inf  -4.328  <.0001
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.2187384 0.07331256 Inf  -2.984  0.0028
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_imm_7, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.5403959 0.07214149 Inf -0.6817907 -0.3990012
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.3150343 0.07279314 Inf -0.4577062 -0.1723623
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.2187384 0.07331256 Inf -0.3624284 -0.0750485
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

7.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_7 <- summary(lsmeans(modelRVLT_imm_7, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTimmediate_lsmeans_7$Time<-NA
RVLTimmediate_lsmeans_7$Time[RVLTimmediate_lsmeans_7$timefactor==1]<-"Baseline"
RVLTimmediate_lsmeans_7$Time[RVLTimmediate_lsmeans_7$timefactor==2]<-"Follow-up 1"
RVLTimmediate_lsmeans_7$Time[RVLTimmediate_lsmeans_7$timefactor==3]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_7, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + 
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

7.1.4) Planned contrasts

lsmeans.RVLTImm3 <- lsmeans(modelRVLT_imm_7, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.RVLTImm3,list(c1st,c2nd,c3rd),by=NULL)

##  contrast                                           estimate         SE  df
##  structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.2253617 0.07961422 Inf
##  structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) 0.0962958 0.08064700 Inf
##  structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) 0.3216575 0.08007053 Inf
##  z.ratio p.value
##    2.831  0.0046
##    1.194  0.2325
##    4.017  0.0001
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

7.2) RVLT Delayed Recall

7.2.1) Model

modelRVLT_del_7<- lmer(RVLT_Delayed_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel +
                        (1|ID), data= Tracking.data_long)
summary(modelRVLT_del_7)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 168972.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8393 -0.5606 -0.0391  0.5104  5.0697 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.642    2.375   
##  Residual             7.751    2.784   
## Number of obs: 32024, groups:  ID, 10961
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             8.698e+00  2.517e-01
## timefactor2                                             5.258e-01  5.618e-02
## timefactor3                                             1.110e+00  5.677e-02
## PandemicFU2 data collected before COVID-19              4.403e-01  7.114e-02
## Age                                                     1.075e-02  2.890e-03
## SexM                                                   -6.076e-01  5.887e-02
## EducationHigh School Diploma                            3.403e-01  8.548e-02
## EducationLess than High School Diploma                  4.722e-01  1.176e-01
## EducationSome College                                   2.878e-01  1.075e-01
## EthnicityWhite                                          8.402e-01  1.694e-01
## IncomeLevel>$150k                                       1.011e+00  1.622e-01
## IncomeLevel$100-150k                                    9.228e-01  1.283e-01
## IncomeLevel$20-50k                                      3.182e-01  8.314e-02
## IncomeLevel$50-100k                                     7.758e-01  8.956e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -3.515e-02  7.631e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -3.266e-01  7.673e-02
##                                                                df t value
## (Intercept)                                             1.132e+04  34.550
## timefactor2                                             2.120e+04   9.360
## timefactor3                                             2.132e+04  19.550
## PandemicFU2 data collected before COVID-19              2.333e+04   6.189
## Age                                                     1.094e+04   3.718
## SexM                                                    1.092e+04 -10.321
## EducationHigh School Diploma                            1.093e+04   3.980
## EducationLess than High School Diploma                  1.106e+04   4.014
## EducationSome College                                   1.087e+04   2.676
## EthnicityWhite                                          1.099e+04   4.960
## IncomeLevel>$150k                                       1.091e+04   6.232
## IncomeLevel$100-150k                                    1.090e+04   7.193
## IncomeLevel$20-50k                                      1.094e+04   3.827
## IncomeLevel$50-100k                                     1.092e+04   8.663
## timefactor2:PandemicFU2 data collected before COVID-19  2.120e+04  -0.461
## timefactor3:PandemicFU2 data collected before COVID-19  2.126e+04  -4.257
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## timefactor3                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             6.17e-10 ***
## Age                                                    0.000202 ***
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                           6.92e-05 ***
## EducationLess than High School Diploma                 6.01e-05 ***
## EducationSome College                                  0.007455 ** 
## EthnicityWhite                                         7.15e-07 ***
## IncomeLevel>$150k                                      4.78e-10 ***
## IncomeLevel$100-150k                                   6.73e-13 ***
## IncomeLevel$20-50k                                     0.000130 ***
## IncomeLevel$50-100k                                     < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.645107    
## timefactor3:PandemicFU2 data collected before COVID-19 2.08e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

7.2.2) Estimated marginal means

lsmeans(modelRVLT_del_7, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.35532 0.1050081 Inf  10.14951  10.56113
##  FU2 data collected before COVID-19 10.79559 0.1054942 Inf  10.58882  11.00235
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.88113 0.1053799 Inf  10.67459  11.08767
##  FU2 data collected before COVID-19 11.28625 0.1058256 Inf  11.07883  11.49366
## 
## timefactor = 3:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.46514 0.1056618 Inf  11.25805  11.67223
##  FU2 data collected before COVID-19 11.57880 0.1057992 Inf  11.37144  11.78617
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_del_7, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.4402639 0.07114179 Inf  -6.189  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.4051153 0.07201606 Inf  -5.625  <.0001
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1136644 0.07247556 Inf  -1.568  0.1168
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_del_7, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.4402639 0.07114179 Inf -0.5796993 -0.30082857
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.4051153 0.07201606 Inf -0.5462642 -0.26396640
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.1136644 0.07247556 Inf -0.2557139  0.02838505
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

7.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_7 <- summary(lsmeans(modelRVLT_del_7, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTdelayed_lsmeans_7$Time<-NA
RVLTdelayed_lsmeans_7$Time[RVLTdelayed_lsmeans_7$timefactor==1]<-"Baseline"
RVLTdelayed_lsmeans_7$Time[RVLTdelayed_lsmeans_7$timefactor==2]<-"Follow-up 1"
RVLTdelayed_lsmeans_7$Time[RVLTdelayed_lsmeans_7$timefactor==3]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_7, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + 
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

7.2.4) Planned contrasts

lsmeans.RVLTDel3 <- lsmeans(modelRVLT_del_7, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.RVLTDel3,list(c1st,c2nd,c3rd),by=NULL)

##  contrast                                           estimate         SE  df
##  structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.0351486 0.07631430 Inf
##  structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) 0.2914509 0.07750896 Inf
##  structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) 0.3265995 0.07672876 Inf
##  z.ratio p.value
##    0.461  0.6451
##    3.760  0.0002
##    4.257  <.0001
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

7.3) Mental Alteration Test

7.3.1) Model

modelMAT_7<- lmer(MAT_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel +
                    (1|ID), data= Tracking.data_long)
summary(modelMAT_7)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 161455.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1003 -0.4734 -0.0185  0.4253  4.8862 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.047    2.247   
##  Residual             7.684    2.772   
## Number of obs: 30761, groups:  ID, 10961
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             8.745e+00  2.449e-01
## timefactor2                                             1.159e+00  5.726e-02
## timefactor3                                            -2.456e-01  5.770e-02
## PandemicFU2 data collected before COVID-19              1.342e-01  6.935e-02
## Age                                                    -2.369e-03  2.813e-03
## SexM                                                   -9.652e-01  5.717e-02
## EducationHigh School Diploma                           -1.402e-02  8.303e-02
## EducationLess than High School Diploma                 -2.118e-01  1.147e-01
## EducationSome College                                  -3.669e-02  1.042e-01
## EthnicityWhite                                          1.321e+00  1.646e-01
## IncomeLevel>$150k                                       8.113e-01  1.573e-01
## IncomeLevel$100-150k                                    1.000e+00  1.244e-01
## IncomeLevel$20-50k                                      3.765e-01  8.080e-02
## IncomeLevel$50-100k                                     7.398e-01  8.700e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -5.205e-01  7.763e-02
## timefactor3:PandemicFU2 data collected before COVID-19  1.626e-02  7.794e-02
##                                                                df t value
## (Intercept)                                             1.130e+04  35.715
## timefactor2                                             2.026e+04  20.236
## timefactor3                                             2.034e+04  -4.257
## PandemicFU2 data collected before COVID-19              2.332e+04   1.935
## Age                                                     1.096e+04  -0.842
## SexM                                                    1.085e+04 -16.882
## EducationHigh School Diploma                            1.086e+04  -0.169
## EducationLess than High School Diploma                  1.111e+04  -1.847
## EducationSome College                                   1.074e+04  -0.352
## EthnicityWhite                                          1.094e+04   8.026
## IncomeLevel>$150k                                       1.081e+04   5.157
## IncomeLevel$100-150k                                    1.080e+04   8.040
## IncomeLevel$20-50k                                      1.090e+04   4.660
## IncomeLevel$50-100k                                     1.086e+04   8.504
## timefactor2:PandemicFU2 data collected before COVID-19  2.023e+04  -6.705
## timefactor3:PandemicFU2 data collected before COVID-19  2.027e+04   0.209
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## timefactor3                                            2.09e-05 ***
## PandemicFU2 data collected before COVID-19               0.0530 .  
## Age                                                      0.3999    
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                             0.8659    
## EducationLess than High School Diploma                   0.0648 .  
## EducationSome College                                    0.7248    
## EthnicityWhite                                         1.11e-15 ***
## IncomeLevel>$150k                                      2.56e-07 ***
## IncomeLevel$100-150k                                   9.96e-16 ***
## IncomeLevel$20-50k                                     3.20e-06 ***
## IncomeLevel$50-100k                                     < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 2.07e-11 ***
## timefactor3:PandemicFU2 data collected before COVID-19   0.8347    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

7.3.2) Estimated marginal means

lsmeans(modelMAT_7, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.297556 0.1020499 Inf  9.097542  9.497570
##  FU2 data collected before COVID-19  9.431782 0.1025383 Inf  9.230810  9.632753
## 
## timefactor = 2:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.456213 0.1031868 Inf 10.253971 10.658456
##  FU2 data collected before COVID-19 10.069931 0.1033760 Inf  9.867317 10.272544
## 
## timefactor = 3:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.051972 0.1033932 Inf  8.849325  9.254619
##  FU2 data collected before COVID-19  9.202458 0.1033700 Inf  8.999857  9.405060
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelMAT_7, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1342255 0.06935486 Inf  -1.935  0.0529
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##   0.3862827 0.07206039 Inf   5.361  <.0001
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1504864 0.07239121 Inf  -2.079  0.0376
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelMAT_7, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.1342255 0.06935486 Inf -0.2701585  0.0017076
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##   0.3862827 0.07206039 Inf  0.2450469  0.5275185
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.1504864 0.07239121 Inf -0.2923706 -0.0086023
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

7.3.3) Graph of estimated marginal means

MAT_lsmeans_7 <- summary(lsmeans(modelMAT_7, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.

MAT_lsmeans_7$Time<-NA
MAT_lsmeans_7$Time[MAT_lsmeans_7$timefactor==1]<-"Baseline"
MAT_lsmeans_7$Time[MAT_lsmeans_7$timefactor==2]<-"Follow-up 1"
MAT_lsmeans_7$Time[MAT_lsmeans_7$timefactor==3]<-"Follow-up 2"
ggplot(MAT_lsmeans_7, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + 
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

7.3.4) Planned contrasts

lsmeans.MAT3 <- lsmeans(modelMAT_7, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.MAT3,list(c1st,c2nd,c3rd),by=NULL)

##  contrast                                            estimate         SE  df
##  structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L))  0.5205082 0.07763266 Inf
##  structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) -0.5367691 0.08021640 Inf
##  structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) -0.0162610 0.07793864 Inf
##  z.ratio p.value
##    6.705  <.0001
##   -6.692  <.0001
##   -0.209  0.8347
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

7.4) Animal Fluency

7.4.1) Model

modelAnimals_7<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel +
                        (1|ID), data= Tracking.data_long)
summary(modelAnimals_7)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## Animal_Fluency_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 151298.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.3310 -0.5632 -0.0182  0.5489  4.7037 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.916    2.217   
##  Residual             3.651    1.911   
## Number of obs: 32347, groups:  ID, 10961
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             9.139e+00  2.158e-01
## timefactor2                                            -7.973e-02  3.837e-02
## timefactor3                                            -1.972e-02  3.882e-02
## PandemicFU2 data collected before COVID-19              2.106e-01  5.702e-02
## Age                                                    -9.285e-03  2.486e-03
## SexM                                                   -2.783e-01  5.066e-02
## EducationHigh School Diploma                            3.197e-01  7.357e-02
## EducationLess than High School Diploma                  4.108e-01  1.009e-01
## EducationSome College                                   2.551e-01  9.261e-02
## EthnicityWhite                                          1.397e+00  1.454e-01
## IncomeLevel>$150k                                       7.018e-01  1.396e-01
## IncomeLevel$100-150k                                    6.896e-01  1.104e-01
## IncomeLevel$20-50k                                      1.830e-01  7.151e-02
## IncomeLevel$50-100k                                     4.821e-01  7.706e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -2.362e-02  5.211e-02
## timefactor3:PandemicFU2 data collected before COVID-19  3.057e-03  5.245e-02
##                                                                df t value
## (Intercept)                                             1.117e+04  42.351
## timefactor2                                             2.144e+04  -2.078
## timefactor3                                             2.153e+04  -0.508
## PandemicFU2 data collected before COVID-19              1.928e+04   3.693
## Age                                                     1.095e+04  -3.735
## SexM                                                    1.094e+04  -5.492
## EducationHigh School Diploma                            1.096e+04   4.345
## EducationLess than High School Diploma                  1.098e+04   4.070
## EducationSome College                                   1.092e+04   2.755
## EthnicityWhite                                          1.094e+04   9.607
## IncomeLevel>$150k                                       1.095e+04   5.029
## IncomeLevel$100-150k                                    1.094e+04   6.244
## IncomeLevel$20-50k                                      1.095e+04   2.559
## IncomeLevel$50-100k                                     1.094e+04   6.256
## timefactor2:PandemicFU2 data collected before COVID-19  2.144e+04  -0.453
## timefactor3:PandemicFU2 data collected before COVID-19  2.149e+04   0.058
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            0.037722 *  
## timefactor3                                            0.611443    
## PandemicFU2 data collected before COVID-19             0.000222 ***
## Age                                                    0.000189 ***
## SexM                                                   4.05e-08 ***
## EducationHigh School Diploma                           1.41e-05 ***
## EducationLess than High School Diploma                 4.74e-05 ***
## EducationSome College                                  0.005879 ** 
## EthnicityWhite                                          < 2e-16 ***
## IncomeLevel>$150k                                      5.02e-07 ***
## IncomeLevel$100-150k                                   4.43e-10 ***
## IncomeLevel$20-50k                                     0.010512 *  
## IncomeLevel$50-100k                                    4.11e-10 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.650419    
## timefactor3:PandemicFU2 data collected before COVID-19 0.953525    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

7.4.2) Estimated marginal means

lsmeans(modelAnimals_7, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  9.784883 0.08876053 Inf  9.610915  9.958850
##  FU2 data collected before COVID-19 9.995461 0.08941223 Inf  9.820216 10.170705
## 
## timefactor = 2:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  9.705153 0.08886856 Inf  9.530974  9.879332
##  FU2 data collected before COVID-19 9.892114 0.08949478 Inf  9.716707 10.067520
## 
## timefactor = 3:
##  Pandemic                             lsmean         SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  9.765159 0.08904435 Inf  9.590635  9.939683
##  FU2 data collected before COVID-19 9.978794 0.08951106 Inf  9.803356 10.154233
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelAnimals_7, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.2105781 0.05702043 Inf  -3.693  0.0002
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1869606 0.05729576 Inf  -3.263  0.0011
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.2136350 0.05761450 Inf  -3.708  0.0002
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelAnimals_7, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.2105781 0.05702043 Inf -0.3223361 -0.09882015
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.1869606 0.05729576 Inf -0.2992583 -0.07466299
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.2136350 0.05761450 Inf -0.3265574 -0.10071268
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

7.4.3) Graph of estimated marginal means

Animals_lsmeans_7 <- summary(lsmeans(modelAnimals_7, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.

Animals_lsmeans_7$Time<-NA
Animals_lsmeans_7$Time[Animals_lsmeans_7$timefactor==1]<-"Baseline"
Animals_lsmeans_7$Time[Animals_lsmeans_7$timefactor==2]<-"Follow-up 1"
Animals_lsmeans_7$Time[Animals_lsmeans_7$timefactor==3]<-"Follow-up 2"
ggplot(Animals_lsmeans_7, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + 
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

7.4.4) Planned contrasts

lsmeans.Animals3 <- lsmeans(modelAnimals_7, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.Animals3,list(c1st,c2nd,c3rd),by=NULL)

##  contrast                                             estimate         SE  df
##  structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L))  0.02361753 0.05211439 Inf
##  structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) -0.02667440 0.05273844 Inf
##  structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) -0.00305687 0.05245070 Inf
##  z.ratio p.value
##    0.453  0.6504
##   -0.506  0.6130
##   -0.058  0.9535
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

8) Simple Adjusted Model: Time x Pandemic effects on cognition, physical activity, and sleep duration (adjusted for baseline)

All models adjusted for baseline, age, sex, education level, ethnicity, income level

8.1) RVLT Immediate Recall

8.1.1) Model

modelRVLT_imm_adj3<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + RVLT_Immediate_Normedbaseline +
                            (1|ID), data= Tracking.data_long_2)
summary(modelRVLT_imm_adj3)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## RVLT_Immediate_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + RVLT_Immediate_Normedbaseline +      (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 111271.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6415 -0.5658 -0.0343  0.5293  3.8307 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.176    2.043   
##  Residual             7.553    2.748   
## Number of obs: 21250, groups:  ID, 10936
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             8.194e+00  2.533e-01
## timefactor2                                             3.090e-01  5.631e-02
## PandemicFU2 data collected before COVID-19              1.905e-01  6.742e-02
## Age                                                    -2.881e-02  2.866e-03
## SexM                                                   -4.640e-01  5.834e-02
## EducationHigh School Diploma                            1.805e-01  8.451e-02
## EducationLess than High School Diploma                  4.577e-01  1.164e-01
## EducationSome College                                   1.508e-01  1.062e-01
## EthnicityWhite                                          5.942e-01  1.672e-01
## IncomeLevel>$150k                                       7.517e-01  1.606e-01
## IncomeLevel$100-150k                                    6.567e-01  1.268e-01
## IncomeLevel$20-50k                                      2.418e-01  8.216e-02
## IncomeLevel$50-100k                                     5.780e-01  8.861e-02
## RVLT_Immediate_Normedbaseline                           3.545e-01  7.244e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -9.245e-02  7.615e-02
##                                                                df t value
## (Intercept)                                             1.109e+04  32.342
## timefactor2                                             1.072e+04   5.487
## PandemicFU2 data collected before COVID-19              1.882e+04   2.826
## Age                                                     1.088e+04 -10.051
## SexM                                                    1.086e+04  -7.954
## EducationHigh School Diploma                            1.088e+04   2.136
## EducationLess than High School Diploma                  1.101e+04   3.932
## EducationSome College                                   1.078e+04   1.420
## EthnicityWhite                                          1.081e+04   3.554
## IncomeLevel>$150k                                       1.085e+04   4.679
## IncomeLevel$100-150k                                    1.082e+04   5.179
## IncomeLevel$20-50k                                      1.088e+04   2.943
## IncomeLevel$50-100k                                     1.086e+04   6.523
## RVLT_Immediate_Normedbaseline                           1.088e+04  48.929
## timefactor2:PandemicFU2 data collected before COVID-19  1.066e+04  -1.214
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            4.18e-08 ***
## PandemicFU2 data collected before COVID-19              0.00472 ** 
## Age                                                     < 2e-16 ***
## SexM                                                   1.98e-15 ***
## EducationHigh School Diploma                            0.03273 *  
## EducationLess than High School Diploma                 8.48e-05 ***
## EducationSome College                                   0.15567    
## EthnicityWhite                                          0.00038 ***
## IncomeLevel>$150k                                      2.91e-06 ***
## IncomeLevel$100-150k                                   2.27e-07 ***
## IncomeLevel$20-50k                                      0.00326 ** 
## IncomeLevel$50-100k                                    7.21e-11 ***
## RVLT_Immediate_Normedbaseline                           < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19  0.22472    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

8.1.2) Estimated marginal means

lsmeans(modelRVLT_imm_adj3, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.83015 0.1026192 Inf  10.62902  11.03128
##  FU2 data collected before COVID-19 11.02067 0.1033453 Inf  10.81812  11.22322
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.13912 0.1029498 Inf  10.93735  11.34090
##  FU2 data collected before COVID-19 11.23719 0.1033229 Inf  11.03468  11.43970
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_imm_adj3, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.19052148 0.06742308 Inf  -2.826  0.0047
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.09806713 0.06796828 Inf  -1.443  0.1491
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_imm_adj3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.19052148 0.06742308 Inf -0.3226683 -0.05837468
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.09806713 0.06796828 Inf -0.2312825  0.03514825
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

8.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_adj3 <- summary(lsmeans(modelRVLT_imm_adj3, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTimmediate_lsmeans_adj3$Time<-NA
RVLTimmediate_lsmeans_adj3$Time[RVLTimmediate_lsmeans_adj3$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj3$Time[RVLTimmediate_lsmeans_adj3$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj3, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

8.1.4) Planned contrasts

lsmeans.RVLTImm4 <- lsmeans(modelRVLT_imm_adj3, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.RVLTImm4,list(c4th),by=NULL)

##  contrast                                      estimate         SE  df z.ratio
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.09245435 0.07614784 Inf   1.214
##  p.value
##   0.2247
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

8.2) RVLT Delayed Recall

8.2.1) Model

modelRVLT_del_adj3<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + RVLT_Delayed_Normedbaseline +
                            (1|ID), data= Tracking.data_long_2)
summary(modelRVLT_del_adj3)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + RVLT_Delayed_Normedbaseline + (1 |      ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 109120.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9429 -0.5473 -0.0380  0.5072  4.1729 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.160    2.040   
##  Residual             7.003    2.646   
## Number of obs: 21063, groups:  ID, 10918
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             7.067e+00  2.505e-01
## timefactor2                                             5.822e-01  5.461e-02
## PandemicFU2 data collected before COVID-19              2.847e-01  6.604e-02
## Age                                                    -1.943e-02  2.828e-03
## SexM                                                   -4.248e-01  5.741e-02
## EducationHigh School Diploma                            2.504e-01  8.320e-02
## EducationLess than High School Diploma                  2.394e-01  1.151e-01
## EducationSome College                                   1.736e-01  1.045e-01
## EthnicityWhite                                          7.156e-01  1.653e-01
## IncomeLevel>$150k                                       6.427e-01  1.579e-01
## IncomeLevel$100-150k                                    5.286e-01  1.249e-01
## IncomeLevel$20-50k                                      2.425e-01  8.096e-02
## IncomeLevel$50-100k                                     6.082e-01  8.723e-02
## RVLT_Delayed_Normedbaseline                             3.992e-01  7.311e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -2.896e-01  7.378e-02
##                                                                df t value
## (Intercept)                                             1.115e+04  28.209
## timefactor2                                             1.065e+04  10.661
## PandemicFU2 data collected before COVID-19              1.856e+04   4.310
## Age                                                     1.090e+04  -6.869
## SexM                                                    1.085e+04  -7.400
## EducationHigh School Diploma                            1.085e+04   3.010
## EducationLess than High School Diploma                  1.108e+04   2.080
## EducationSome College                                   1.075e+04   1.662
## EthnicityWhite                                          1.092e+04   4.328
## IncomeLevel>$150k                                       1.083e+04   4.071
## IncomeLevel$100-150k                                    1.082e+04   4.234
## IncomeLevel$20-50k                                      1.089e+04   2.995
## IncomeLevel$50-100k                                     1.085e+04   6.972
## RVLT_Delayed_Normedbaseline                             1.084e+04  54.595
## timefactor2:PandemicFU2 data collected before COVID-19  1.058e+04  -3.925
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             1.64e-05 ***
## Age                                                    6.84e-12 ***
## SexM                                                   1.46e-13 ***
## EducationHigh School Diploma                            0.00262 ** 
## EducationLess than High School Diploma                  0.03751 *  
## EducationSome College                                   0.09657 .  
## EthnicityWhite                                         1.52e-05 ***
## IncomeLevel>$150k                                      4.71e-05 ***
## IncomeLevel$100-150k                                   2.32e-05 ***
## IncomeLevel$20-50k                                      0.00275 ** 
## IncomeLevel$50-100k                                    3.31e-12 ***
## RVLT_Delayed_Normedbaseline                             < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 8.72e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

8.2.2) Estimated marginal means

lsmeans(modelRVLT_del_adj3, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.86661 0.1012336 Inf  10.66820  11.06503
##  FU2 data collected before COVID-19 11.15128 0.1019189 Inf  10.95152  11.35103
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.44886 0.1014974 Inf  11.24993  11.64779
##  FU2 data collected before COVID-19 11.44389 0.1018863 Inf  11.24420  11.64359
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_del_adj3, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.28466347 0.06604078 Inf  -4.310  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##   0.00496514 0.06653229 Inf   0.075  0.9405
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_del_adj3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.28466347 0.06604078 Inf -0.4141010 -0.1552259
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL  asymp.UCL
##   0.00496514 0.06653229 Inf -0.1254358  0.1353660
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

8.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_adj3 <- summary(lsmeans(modelRVLT_del_adj3, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTdelayed_lsmeans_adj3$Time<-NA
RVLTdelayed_lsmeans_adj3$Time[RVLTdelayed_lsmeans_adj3$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj3$Time[RVLTdelayed_lsmeans_adj3$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj3, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

8.2.4) Planned contrasts

lsmeans.RVLTDel4 <- lsmeans(modelRVLT_del_adj3, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.RVLTDel4,list(c4th),by=NULL)

##  contrast                                     estimate         SE  df z.ratio
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.2896286 0.07378406 Inf   3.925
##  p.value
##   0.0001
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

8.3) Mental Alteration Test

8.3.1) Model

modelMAT_adj3<- lmer(MAT_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + MAT_Normedbaseline +
                      (1|ID), data= Tracking.data_long_2)
summary(modelMAT_adj3)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + MAT_Normedbaseline + (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 102961.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4724 -0.5194 -0.0813  0.3623  4.8819 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.395    1.548   
##  Residual             8.438    2.905   
## Number of obs: 19800, groups:  ID, 10792
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             7.143e+00  2.420e-01
## timefactor2                                            -1.408e+00  6.210e-02
## PandemicFU2 data collected before COVID-19             -5.115e-01  6.711e-02
## Age                                                    -1.415e-02  2.710e-03
## SexM                                                   -1.122e+00  5.481e-02
## EducationHigh School Diploma                           -6.998e-02  7.956e-02
## EducationLess than High School Diploma                 -2.842e-01  1.114e-01
## EducationSome College                                  -1.918e-01  9.920e-02
## EthnicityWhite                                          8.658e-01  1.585e-01
## IncomeLevel>$150k                                       1.143e-01  1.506e-01
## IncomeLevel$100-150k                                    1.672e-01  1.193e-01
## IncomeLevel$20-50k                                      1.217e-01  7.764e-02
## IncomeLevel$50-100k                                     1.579e-01  8.366e-02
## MAT_Normedbaseline                                      4.487e-01  7.518e-03
## timefactor2:PandemicFU2 data collected before COVID-19  5.431e-01  8.382e-02
##                                                                df t value
## (Intercept)                                             1.091e+04  29.518
## timefactor2                                             1.022e+04 -22.677
## PandemicFU2 data collected before COVID-19              1.904e+04  -7.622
## Age                                                     1.069e+04  -5.221
## SexM                                                    1.052e+04 -20.466
## EducationHigh School Diploma                            1.050e+04  -0.880
## EducationLess than High School Diploma                  1.081e+04  -2.551
## EducationSome College                                   1.035e+04  -1.934
## EthnicityWhite                                          1.066e+04   5.462
## IncomeLevel>$150k                                       1.044e+04   0.759
## IncomeLevel$100-150k                                    1.046e+04   1.402
## IncomeLevel$20-50k                                      1.058e+04   1.567
## IncomeLevel$50-100k                                     1.051e+04   1.887
## MAT_Normedbaseline                                      1.057e+04  59.680
## timefactor2:PandemicFU2 data collected before COVID-19  1.013e+04   6.480
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             2.61e-14 ***
## Age                                                    1.81e-07 ***
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                             0.3791    
## EducationLess than High School Diploma                   0.0107 *  
## EducationSome College                                    0.0532 .  
## EthnicityWhite                                         4.81e-08 ***
## IncomeLevel>$150k                                        0.4480    
## IncomeLevel$100-150k                                     0.1610    
## IncomeLevel$20-50k                                       0.1171    
## IncomeLevel$50-100k                                      0.0591 .  
## MAT_Normedbaseline                                      < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 9.58e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

8.3.2) Estimated marginal means

lsmeans(modelMAT_adj3, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                              lsmean         SE  df asymp.LCL
##  FU2 data collected after COVID-19  10.628611 0.09840109 Inf 10.435748
##  FU2 data collected before COVID-19 10.117090 0.09865595 Inf  9.923728
##  asymp.UCL
##  10.821473
##  10.310452
## 
## timefactor = 2:
##  Pandemic                              lsmean         SE  df asymp.LCL
##  FU2 data collected after COVID-19   9.220413 0.09860619 Inf  9.027148
##  FU2 data collected before COVID-19  9.252042 0.09865478 Inf  9.058682
##  asymp.UCL
##   9.413678
##   9.445402
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelMAT_adj3, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##   0.5115203 0.06710904 Inf   7.622  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.0316291 0.06748026 Inf  -0.469  0.6393
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelMAT_adj3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df asymp.LCL asymp.UCL
##   0.5115203 0.06710904 Inf  0.379989 0.6430516
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df asymp.LCL asymp.UCL
##  -0.0316291 0.06748026 Inf -0.163888 0.1006298
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

8.3.3) Graph of estimated marginal means

MAT_lsmeans_adj3 <- summary(lsmeans(modelMAT_adj3, ~Pandemic|timefactor))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.

MAT_lsmeans_adj3$Time<-NA
MAT_lsmeans_adj3$Time[MAT_lsmeans_adj3$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj3$Time[MAT_lsmeans_adj3$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj3, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

8.3.4) Planned contrasts

lsmeans.MAT4 <- lsmeans(modelMAT_adj3, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.MAT4,list(c4th),by=NULL)

##  contrast                                      estimate         SE  df z.ratio
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.5431494 0.08381608 Inf  -6.480
##  p.value
##   <.0001
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

8.4) Animal Fluency

8.4.1) Model

modelAnimals_adj3<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + Animal_Fluency_Normedbaseline +
                           (1|ID), data= Tracking.data_long_2)
summary(modelAnimals_adj3)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## Animal_Fluency_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + Animal_Fluency_Normedbaseline +      (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 95445.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9116 -0.5510 -0.0211  0.5305  4.7530 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.387    1.545   
##  Residual             3.202    1.789   
## Number of obs: 21386, groups:  ID, 10943
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             5.335e+00  1.823e-01
## timefactor2                                             5.965e-02  3.660e-02
## PandemicFU2 data collected before COVID-19              1.088e-01  4.639e-02
## Age                                                    -2.011e-02  2.016e-03
## SexM                                                   -1.487e-01  4.110e-02
## EducationHigh School Diploma                            2.225e-01  5.973e-02
## EducationLess than High School Diploma                  1.072e-01  8.206e-02
## EducationSome College                                   1.838e-01  7.499e-02
## EthnicityWhite                                          6.034e-01  1.183e-01
## IncomeLevel>$150k                                       1.189e-01  1.133e-01
## IncomeLevel$100-150k                                    1.489e-01  8.968e-02
## IncomeLevel$20-50k                                      1.515e-03  5.802e-02
## IncomeLevel$50-100k                                     1.656e-01  6.256e-02
## Animal_Fluency_Normedbaseline                           5.260e-01  6.297e-03
## timefactor2:PandemicFU2 data collected before COVID-19  2.642e-02  4.942e-02
##                                                                df t value
## (Intercept)                                             1.108e+04  29.261
## timefactor2                                             1.076e+04   1.630
## PandemicFU2 data collected before COVID-19              1.803e+04   2.346
## Age                                                     1.089e+04  -9.975
## SexM                                                    1.087e+04  -3.617
## EducationHigh School Diploma                            1.089e+04   3.725
## EducationLess than High School Diploma                  1.098e+04   1.307
## EducationSome College                                   1.082e+04   2.451
## EthnicityWhite                                          1.085e+04   5.103
## IncomeLevel>$150k                                       1.087e+04   1.049
## IncomeLevel$100-150k                                    1.086e+04   1.660
## IncomeLevel$20-50k                                      1.088e+04   0.026
## IncomeLevel$50-100k                                     1.087e+04   2.647
## Animal_Fluency_Normedbaseline                           1.086e+04  83.529
## timefactor2:PandemicFU2 data collected before COVID-19  1.068e+04   0.535
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            0.103178    
## PandemicFU2 data collected before COVID-19             0.018968 *  
## Age                                                     < 2e-16 ***
## SexM                                                   0.000300 ***
## EducationHigh School Diploma                           0.000196 ***
## EducationLess than High School Diploma                 0.191407    
## EducationSome College                                  0.014258 *  
## EthnicityWhite                                          3.4e-07 ***
## IncomeLevel>$150k                                      0.294009    
## IncomeLevel$100-150k                                   0.096843 .  
## IncomeLevel$20-50k                                     0.979174    
## IncomeLevel$50-100k                                    0.008135 ** 
## Animal_Fluency_Normedbaseline                           < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.592928    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

8.4.2) Estimated marginal means

lsmeans(modelAnimals_adj3, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                              lsmean         SE  df asymp.LCL
##  FU2 data collected after COVID-19   9.964733 0.07208383 Inf  9.823451
##  FU2 data collected before COVID-19 10.073574 0.07255307 Inf  9.931373
##  asymp.UCL
##   10.10601
##   10.21578
## 
## timefactor = 2:
##  Pandemic                              lsmean         SE  df asymp.LCL
##  FU2 data collected after COVID-19  10.024383 0.07226703 Inf  9.882742
##  FU2 data collected before COVID-19 10.159647 0.07258012 Inf 10.017393
##  asymp.UCL
##   10.16602
##   10.30190
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelAnimals_adj3, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1088415 0.04638720 Inf  -2.346  0.0190
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1352646 0.04675466 Inf  -2.893  0.0038
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelAnimals_adj3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.1088415 0.04638720 Inf -0.1997587 -0.01792425
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.1352646 0.04675466 Inf -0.2269021 -0.04362719
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

8.4.3) Graph of estimated marginal means

Animals_lsmeans_adj3 <- summary(lsmeans(modelAnimals_adj3, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.

Animals_lsmeans_adj3$Time<-NA
Animals_lsmeans_adj3$Time[Animals_lsmeans_adj3$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj3$Time[Animals_lsmeans_adj3$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj3, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

8.3.4) Planned contrasts

lsmeans.Animals4 <- lsmeans(modelAnimals_adj3, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.Animals4,list(c4th),by=NULL)

##  contrast                                       estimate         SE  df z.ratio
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.02642315 0.04942468 Inf  -0.535
##  p.value
##   0.5929
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel 
## Degrees-of-freedom method: asymptotic

9) Full Adjusted Model: Time x Pandemic effects on cognition and physical activity (not adjusting for baseline)

All models use normalized cognitive scores.Each model is adjusted for baseline age, sex, education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, and baseline PASE score

emm_options(rg.limit = 175000)

9.1) RVLT Immediate Recall

9.1.1) Model

modelRVLT_imm_8<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                         (1|ID), data= Tracking.data_long)
summary(modelRVLT_imm_8)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## RVLT_Immediate_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 132322.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6230 -0.5804 -0.0448  0.5230  5.1001 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.227    2.286   
##  Residual             8.338    2.888   
## Number of obs: 24887, groups:  ID, 8467
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             8.092e+00  4.293e-01
## timefactor2                                             7.000e-01  6.641e-02
## timefactor3                                             1.013e+00  6.709e-02
## PandemicFU2 data collected before COVID-19              5.421e-01  8.161e-02
## Age                                                     1.697e-02  4.072e-03
## SexM                                                   -7.871e-01  6.955e-02
## EducationHigh School Diploma                            2.312e-01  9.790e-02
## EducationLess than High School Diploma                  7.346e-01  1.383e-01
## EducationSome College                                   3.476e-01  1.218e-01
## EthnicityWhite                                          9.632e-01  1.941e-01
## IncomeLevel>$150k                                       8.978e-01  1.819e-01
## IncomeLevel$100-150k                                    5.815e-01  1.453e-01
## IncomeLevel$20-50k                                      8.931e-02  9.675e-02
## IncomeLevel$50-100k                                     5.424e-01  1.042e-01
## BMI                                                    -1.859e-02  6.571e-03
## CESD.20.1                                              -3.552e-02  7.818e-03
## SmokingStatusFormer Smoker                              1.920e-01  1.334e-01
## SmokingStatusNever Smoked                               3.584e-01  1.388e-01
## SmokingStatusOccasional Smoker                          2.849e-01  2.628e-01
## RelationshipstatusMarried                               1.057e-01  1.137e-01
## RelationshipstatusSeparated                            -1.018e-01  2.239e-01
## RelationshipstatusSingle                               -7.809e-02  1.536e-01
## RelationshipstatusWidowed                              -1.043e-01  1.553e-01
## LivingstatusAssisted Living                            -6.438e-01  4.609e-01
## LivingstatusHouse                                       1.093e-01  1.024e-01
## LivingstatusOther                                      -3.383e-01  3.822e-01
## AnxietyYes                                             -1.932e-02  1.367e-01
## MoodDisordYes                                          -1.291e-01  9.913e-02
## Chronicconditions                                      -3.727e-02  1.602e-02
## PASE_TOTALbaseline                                      3.077e-03  4.664e-04
## timefactor2:PandemicFU2 data collected before COVID-19 -2.336e-01  8.985e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -3.202e-01  9.033e-02
##                                                                df t value
## (Intercept)                                             8.532e+03  18.849
## timefactor2                                             1.648e+04  10.541
## timefactor3                                             1.658e+04  15.104
## PandemicFU2 data collected before COVID-19              1.881e+04   6.642
## Age                                                     8.410e+03   4.166
## SexM                                                    8.405e+03 -11.316
## EducationHigh School Diploma                            8.410e+03   2.361
## EducationLess than High School Diploma                  8.469e+03   5.312
## EducationSome College                                   8.372e+03   2.854
## EthnicityWhite                                          8.367e+03   4.962
## IncomeLevel>$150k                                       8.408e+03   4.937
## IncomeLevel$100-150k                                    8.380e+03   4.001
## IncomeLevel$20-50k                                      8.412e+03   0.923
## IncomeLevel$50-100k                                     8.409e+03   5.204
## BMI                                                     8.393e+03  -2.829
## CESD.20.1                                               8.403e+03  -4.543
## SmokingStatusFormer Smoker                              8.409e+03   1.439
## SmokingStatusNever Smoked                               8.409e+03   2.582
## SmokingStatusOccasional Smoker                          8.357e+03   1.084
## RelationshipstatusMarried                               8.417e+03   0.930
## RelationshipstatusSeparated                             8.448e+03  -0.455
## RelationshipstatusSingle                                8.410e+03  -0.508
## RelationshipstatusWidowed                               8.423e+03  -0.672
## LivingstatusAssisted Living                             8.432e+03  -1.397
## LivingstatusHouse                                       8.412e+03   1.067
## LivingstatusOther                                       8.349e+03  -0.885
## AnxietyYes                                              8.402e+03  -0.141
## MoodDisordYes                                           8.403e+03  -1.303
## Chronicconditions                                       8.397e+03  -2.326
## PASE_TOTALbaseline                                      8.401e+03   6.597
## timefactor2:PandemicFU2 data collected before COVID-19  1.649e+04  -2.600
## timefactor3:PandemicFU2 data collected before COVID-19  1.654e+04  -3.545
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## timefactor3                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             3.18e-11 ***
## Age                                                    3.13e-05 ***
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                           0.018232 *  
## EducationLess than High School Diploma                 1.11e-07 ***
## EducationSome College                                  0.004325 ** 
## EthnicityWhite                                         7.11e-07 ***
## IncomeLevel>$150k                                      8.10e-07 ***
## IncomeLevel$100-150k                                   6.35e-05 ***
## IncomeLevel$20-50k                                     0.355947    
## IncomeLevel$50-100k                                    2.00e-07 ***
## BMI                                                    0.004682 ** 
## CESD.20.1                                              5.63e-06 ***
## SmokingStatusFormer Smoker                             0.150085    
## SmokingStatusNever Smoked                              0.009843 ** 
## SmokingStatusOccasional Smoker                         0.278288    
## RelationshipstatusMarried                              0.352565    
## RelationshipstatusSeparated                            0.649467    
## RelationshipstatusSingle                               0.611151    
## RelationshipstatusWidowed                              0.501874    
## LivingstatusAssisted Living                            0.162528    
## LivingstatusHouse                                      0.285969    
## LivingstatusOther                                      0.376102    
## AnxietyYes                                             0.887554    
## MoodDisordYes                                          0.192732    
## Chronicconditions                                      0.020038 *  
## PASE_TOTALbaseline                                     4.46e-11 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.009323 ** 
## timefactor3:PandemicFU2 data collected before COVID-19 0.000393 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

9.1.2) Estimated marginal means

lsmeans(modelRVLT_imm_8, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.597492 0.2109562 Inf  9.184025  10.01096
##  FU2 data collected before COVID-19 10.139580 0.2108650 Inf  9.726292  10.55287
## 
## timefactor = 2:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.297469 0.2110827 Inf  9.883755  10.71118
##  FU2 data collected before COVID-19 10.605931 0.2110372 Inf 10.192306  11.01956
## 
## timefactor = 3:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.610743 0.2113088 Inf 10.196585  11.02490
##  FU2 data collected before COVID-19 10.832585 0.2109641 Inf 10.419103  11.24607
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_imm_8, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.5420881 0.08161399 Inf  -6.642  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.3084620 0.08236585 Inf  -3.745  0.0002
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.2218424 0.08290923 Inf  -2.676  0.0075
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_imm_8, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.5420881 0.08161399 Inf -0.7020486 -0.3821276
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.3084620 0.08236585 Inf -0.4698961 -0.1470279
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.2218424 0.08290923 Inf -0.3843416 -0.0593433
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

9.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_8 <- summary(lsmeans(modelRVLT_imm_8, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTimmediate_lsmeans_8$Time<-NA
RVLTimmediate_lsmeans_8$Time[RVLTimmediate_lsmeans_8$timefactor==1]<-"Baseline"
RVLTimmediate_lsmeans_8$Time[RVLTimmediate_lsmeans_8$timefactor==2]<-"Follow-up 1"
RVLTimmediate_lsmeans_8$Time[RVLTimmediate_lsmeans_8$timefactor==3]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_8, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + 
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "RVLT Immediate Score", title = "RVLT Immediate Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

9.1.4) Planned contrasts

lsmeans.RVLTImm8 <- lsmeans(modelRVLT_imm_8, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.RVLTImm8,list(c1st,c2nd,c3rd),by=NULL)

##  contrast                                           estimate         SE  df
##  structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.2336261 0.08984582 Inf
##  structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) 0.0866195 0.09099025 Inf
##  structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) 0.3202457 0.09032617 Inf
##  z.ratio p.value
##    2.600  0.0093
##    0.952  0.3411
##    3.545  0.0004
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

9.2) RVLT Delayed Recall

9.2.1) Model

modelRVLT_del_8<- lmer(RVLT_Delayed_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= Tracking.data_long)
summary(modelRVLT_del_8)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 130446.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8037 -0.5658 -0.0375  0.5152  5.0752 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.527    2.351   
##  Residual             7.723    2.779   
## Number of obs: 24749, groups:  ID, 8467
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             8.007e+00  4.324e-01
## timefactor2                                             5.565e-01  6.415e-02
## timefactor3                                             1.144e+00  6.477e-02
## PandemicFU2 data collected before COVID-19              4.631e-01  8.071e-02
## Age                                                     2.747e-02  4.101e-03
## SexM                                                   -7.259e-01  7.002e-02
## EducationHigh School Diploma                            4.000e-01  9.856e-02
## EducationLess than High School Diploma                  6.261e-01  1.394e-01
## EducationSome College                                   4.204e-01  1.226e-01
## EthnicityWhite                                          8.938e-01  1.960e-01
## IncomeLevel>$150k                                       7.253e-01  1.830e-01
## IncomeLevel$100-150k                                    6.365e-01  1.463e-01
## IncomeLevel$20-50k                                      1.841e-01  9.742e-02
## IncomeLevel$50-100k                                     5.737e-01  1.049e-01
## BMI                                                    -1.728e-02  6.615e-03
## CESD.20.1                                              -3.505e-02  7.875e-03
## SmokingStatusFormer Smoker                             -3.424e-02  1.343e-01
## SmokingStatusNever Smoked                               2.258e-01  1.397e-01
## SmokingStatusOccasional Smoker                          1.609e-01  2.647e-01
## RelationshipstatusMarried                              -1.034e-02  1.145e-01
## RelationshipstatusSeparated                            -4.531e-02  2.254e-01
## RelationshipstatusSingle                               -1.684e-01  1.546e-01
## RelationshipstatusWidowed                              -1.316e-01  1.563e-01
## LivingstatusAssisted Living                            -1.121e+00  4.643e-01
## LivingstatusHouse                                       7.413e-02  1.032e-01
## LivingstatusOther                                      -7.573e-02  3.852e-01
## AnxietyYes                                              1.417e-02  1.376e-01
## MoodDisordYes                                          -1.363e-01  9.978e-02
## Chronicconditions                                      -4.778e-02  1.613e-02
## PASE_TOTALbaseline                                      3.187e-03  4.696e-04
## timefactor2:PandemicFU2 data collected before COVID-19 -6.479e-02  8.674e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -3.469e-01  8.719e-02
##                                                                df t value
## (Intercept)                                             8.543e+03  18.516
## timefactor2                                             1.638e+04   8.674
## timefactor3                                             1.647e+04  17.655
## PandemicFU2 data collected before COVID-19              1.803e+04   5.738
## Age                                                     8.419e+03   6.698
## SexM                                                    8.404e+03 -10.367
## EducationHigh School Diploma                            8.411e+03   4.058
## EducationLess than High School Diploma                  8.511e+03   4.491
## EducationSome College                                   8.367e+03   3.430
## EthnicityWhite                                          8.447e+03   4.561
## IncomeLevel>$150k                                       8.400e+03   3.963
## IncomeLevel$100-150k                                    8.384e+03   4.350
## IncomeLevel$20-50k                                      8.419e+03   1.890
## IncomeLevel$50-100k                                     8.409e+03   5.467
## BMI                                                     8.390e+03  -2.612
## CESD.20.1                                               8.419e+03  -4.450
## SmokingStatusFormer Smoker                              8.400e+03  -0.255
## SmokingStatusNever Smoked                               8.400e+03   1.616
## SmokingStatusOccasional Smoker                          8.376e+03   0.608
## RelationshipstatusMarried                               8.418e+03  -0.090
## RelationshipstatusSeparated                             8.459e+03  -0.201
## RelationshipstatusSingle                                8.402e+03  -1.089
## RelationshipstatusWidowed                               8.422e+03  -0.842
## LivingstatusAssisted Living                             8.451e+03  -2.414
## LivingstatusHouse                                       8.424e+03   0.719
## LivingstatusOther                                       8.385e+03  -0.197
## AnxietyYes                                              8.401e+03   0.103
## MoodDisordYes                                           8.398e+03  -1.366
## Chronicconditions                                       8.402e+03  -2.962
## PASE_TOTALbaseline                                      8.405e+03   6.786
## timefactor2:PandemicFU2 data collected before COVID-19  1.638e+04  -0.747
## timefactor3:PandemicFU2 data collected before COVID-19  1.642e+04  -3.979
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## timefactor3                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             9.71e-09 ***
## Age                                                    2.24e-11 ***
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                           4.99e-05 ***
## EducationLess than High School Diploma                 7.19e-06 ***
## EducationSome College                                  0.000607 ***
## EthnicityWhite                                         5.17e-06 ***
## IncomeLevel>$150k                                      7.46e-05 ***
## IncomeLevel$100-150k                                   1.38e-05 ***
## IncomeLevel$20-50k                                     0.058765 .  
## IncomeLevel$50-100k                                    4.70e-08 ***
## BMI                                                    0.009021 ** 
## CESD.20.1                                              8.68e-06 ***
## SmokingStatusFormer Smoker                             0.798693    
## SmokingStatusNever Smoked                              0.106064    
## SmokingStatusOccasional Smoker                         0.543271    
## RelationshipstatusMarried                              0.928035    
## RelationshipstatusSeparated                            0.840732    
## RelationshipstatusSingle                               0.276040    
## RelationshipstatusWidowed                              0.399758    
## LivingstatusAssisted Living                            0.015787 *  
## LivingstatusHouse                                      0.472411    
## LivingstatusOther                                      0.844147    
## AnxietyYes                                             0.917977    
## MoodDisordYes                                          0.171981    
## Chronicconditions                                      0.003066 ** 
## PASE_TOTALbaseline                                     1.23e-11 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.455094    
## timefactor3:PandemicFU2 data collected before COVID-19 6.96e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

9.2.2) Estimated marginal means

lsmeans(modelRVLT_del_8, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.00794 0.2122547 Inf  9.591932  10.42396
##  FU2 data collected before COVID-19 10.47108 0.2122193 Inf 10.055139  10.88702
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.56442 0.2124816 Inf 10.147960  10.98087
##  FU2 data collected before COVID-19 10.96276 0.2124253 Inf 10.546414  11.37911
## 
## timefactor = 3:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.15146 0.2126820 Inf 10.734615  11.56831
##  FU2 data collected before COVID-19 11.26772 0.2123699 Inf 10.851481  11.68396
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_del_8, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.4631368 0.08070721 Inf  -5.738  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.3983447 0.08169290 Inf  -4.876  <.0001
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1162545 0.08219608 Inf  -1.414  0.1573
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_del_8, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.4631368 0.08070721 Inf -0.6213201 -0.30495362
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.3983447 0.08169290 Inf -0.5584598 -0.23822954
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.1162545 0.08219608 Inf -0.2773558  0.04484687
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

9.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_8 <- summary(lsmeans(modelRVLT_del_8, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTdelayed_lsmeans_8$Time<-NA
RVLTdelayed_lsmeans_8$Time[RVLTdelayed_lsmeans_8$timefactor==1]<-"Baseline"
RVLTdelayed_lsmeans_8$Time[RVLTdelayed_lsmeans_8$timefactor==2]<-"Follow-up 1"
RVLTdelayed_lsmeans_8$Time[RVLTdelayed_lsmeans_8$timefactor==3]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_8, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + 
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "RVLT Delayed Score", title = "RVLT Delayed Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

9.2.4) Planned contrasts

lsmeans.RVLTDel8 <- lsmeans(modelRVLT_del_8, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.RVLTDel8,list(c1st,c2nd,c3rd),by=NULL)

##  contrast                                           estimate         SE  df
##  structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.0647922 0.08674026 Inf
##  structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) 0.2820902 0.08807138 Inf
##  structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) 0.3468824 0.08718526 Inf
##  z.ratio p.value
##    0.747  0.4551
##    3.203  0.0014
##    3.979  0.0001
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

9.3) Mental Alteration Test

9.3.1) Model

modelMAT_8<- lmer(MAT_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                    (1|ID), data= Tracking.data_long)
summary(modelMAT_8)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 124671.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0904 -0.4700 -0.0150  0.4268  4.9654 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.976    2.231   
##  Residual             7.551    2.748   
## Number of obs: 23820, groups:  ID, 8467
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             9.839e+00  4.199e-01
## timefactor2                                             1.089e+00  6.490e-02
## timefactor3                                            -2.735e-01  6.533e-02
## PandemicFU2 data collected before COVID-19              5.771e-02  7.847e-02
## Age                                                    -1.312e-03  3.987e-03
## SexM                                                   -1.035e+00  6.793e-02
## EducationHigh School Diploma                            5.987e-03  9.568e-02
## EducationLess than High School Diploma                  3.372e-02  1.359e-01
## EducationSome College                                   5.522e-02  1.187e-01
## EthnicityWhite                                          1.339e+00  1.905e-01
## IncomeLevel>$150k                                       9.128e-01  1.776e-01
## IncomeLevel$100-150k                                    9.812e-01  1.419e-01
## IncomeLevel$20-50k                                      3.672e-01  9.463e-02
## IncomeLevel$50-100k                                     7.386e-01  1.019e-01
## BMI                                                    -2.572e-02  6.409e-03
## CESD.20.1                                              -4.432e-02  7.635e-03
## SmokingStatusFormer Smoker                              1.818e-01  1.301e-01
## SmokingStatusNever Smoked                               1.273e-01  1.354e-01
## SmokingStatusOccasional Smoker                          1.427e-01  2.567e-01
## RelationshipstatusMarried                               1.923e-01  1.112e-01
## RelationshipstatusSeparated                            -1.579e-01  2.184e-01
## RelationshipstatusSingle                                2.988e-01  1.500e-01
## RelationshipstatusWidowed                              -1.158e-01  1.520e-01
## LivingstatusAssisted Living                            -7.079e-01  4.507e-01
## LivingstatusHouse                                      -1.705e-01  1.002e-01
## LivingstatusOther                                      -3.714e-01  3.746e-01
## AnxietyYes                                              9.797e-02  1.333e-01
## MoodDisordYes                                           2.718e-01  9.668e-02
## Chronicconditions                                      -6.300e-02  1.568e-02
## PASE_TOTALbaseline                                     -9.834e-04  4.551e-04
## timefactor2:PandemicFU2 data collected before COVID-19 -4.400e-01  8.757e-02
## timefactor3:PandemicFU2 data collected before COVID-19  6.328e-02  8.786e-02
##                                                                df t value
## (Intercept)                                             8.502e+03  23.435
## timefactor2                                             1.569e+04  16.774
## timefactor3                                             1.575e+04  -4.187
## PandemicFU2 data collected before COVID-19              1.795e+04   0.735
## Age                                                     8.412e+03  -0.329
## SexM                                                    8.346e+03 -15.230
## EducationHigh School Diploma                            8.363e+03   0.063
## EducationLess than High School Diploma                  8.559e+03   0.248
## EducationSome College                                   8.270e+03   0.465
## EthnicityWhite                                          8.436e+03   7.030
## IncomeLevel>$150k                                       8.342e+03   5.140
## IncomeLevel$100-150k                                    8.312e+03   6.917
## IncomeLevel$20-50k                                      8.392e+03   3.880
## IncomeLevel$50-100k                                     8.371e+03   7.248
## BMI                                                     8.290e+03  -4.014
## CESD.20.1                                               8.333e+03  -5.805
## SmokingStatusFormer Smoker                              8.311e+03   1.397
## SmokingStatusNever Smoked                               8.311e+03   0.940
## SmokingStatusOccasional Smoker                          8.291e+03   0.556
## RelationshipstatusMarried                               8.379e+03   1.730
## RelationshipstatusSeparated                             8.369e+03  -0.723
## RelationshipstatusSingle                                8.356e+03   1.991
## RelationshipstatusWidowed                               8.421e+03  -0.762
## LivingstatusAssisted Living                             8.384e+03  -1.571
## LivingstatusHouse                                       8.399e+03  -1.701
## LivingstatusOther                                       8.387e+03  -0.991
## AnxietyYes                                              8.313e+03   0.735
## MoodDisordYes                                           8.305e+03   2.811
## Chronicconditions                                       8.386e+03  -4.019
## PASE_TOTALbaseline                                      8.315e+03  -2.161
## timefactor2:PandemicFU2 data collected before COVID-19  1.566e+04  -5.024
## timefactor3:PandemicFU2 data collected before COVID-19  1.569e+04   0.720
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## timefactor3                                            2.85e-05 ***
## PandemicFU2 data collected before COVID-19             0.462063    
## Age                                                    0.742155    
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                           0.950105    
## EducationLess than High School Diploma                 0.804084    
## EducationSome College                                  0.641873    
## EthnicityWhite                                         2.22e-12 ***
## IncomeLevel>$150k                                      2.81e-07 ***
## IncomeLevel$100-150k                                   4.95e-12 ***
## IncomeLevel$20-50k                                     0.000105 ***
## IncomeLevel$50-100k                                    4.60e-13 ***
## BMI                                                    6.03e-05 ***
## CESD.20.1                                              6.67e-09 ***
## SmokingStatusFormer Smoker                             0.162483    
## SmokingStatusNever Smoked                              0.347271    
## SmokingStatusOccasional Smoker                         0.578360    
## RelationshipstatusMarried                              0.083696 .  
## RelationshipstatusSeparated                            0.469871    
## RelationshipstatusSingle                               0.046462 *  
## RelationshipstatusWidowed                              0.446282    
## LivingstatusAssisted Living                            0.116282    
## LivingstatusHouse                                      0.088956 .  
## LivingstatusOther                                      0.321493    
## AnxietyYes                                             0.462461    
## MoodDisordYes                                          0.004944 ** 
## Chronicconditions                                      5.91e-05 ***
## PASE_TOTALbaseline                                     0.030741 *  
## timefactor2:PandemicFU2 data collected before COVID-19 5.11e-07 ***
## timefactor3:PandemicFU2 data collected before COVID-19 0.471359    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

9.3.2) Estimated marginal means

lsmeans(modelMAT_8, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.313348 0.2061680 Inf  8.909266  9.717430
##  FU2 data collected before COVID-19  9.371061 0.2061532 Inf  8.967008  9.775114
## 
## timefactor = 2:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.402054 0.2069456 Inf  9.996448 10.807660
##  FU2 data collected before COVID-19 10.019796 0.2066847 Inf  9.614701 10.424891
## 
## timefactor = 3:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.039853 0.2068304 Inf  8.634472  9.445233
##  FU2 data collected before COVID-19  9.160847 0.2065923 Inf  8.755933  9.565760
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelMAT_8, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.0577128 0.07847061 Inf  -0.735  0.4621
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##   0.3822583 0.08144610 Inf   4.693  <.0001
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1209942 0.08174658 Inf  -1.480  0.1388
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelMAT_8, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL asymp.UCL
##  -0.0577128 0.07847061 Inf -0.2115123 0.0960868
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL asymp.UCL
##   0.3822583 0.08144610 Inf  0.2226269 0.5418897
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL asymp.UCL
##  -0.1209942 0.08174658 Inf -0.2812146 0.0392261
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

9.3.3) Graph of estimated marginal means

MAT_lsmeans_8 <- summary(lsmeans(modelMAT_8, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.

MAT_lsmeans_8$Time<-NA
MAT_lsmeans_8$Time[MAT_lsmeans_8$timefactor==1]<-"Baseline"
MAT_lsmeans_8$Time[MAT_lsmeans_8$timefactor==2]<-"Follow-up 1"
MAT_lsmeans_8$Time[MAT_lsmeans_8$timefactor==3]<-"Follow-up 2"
ggplot(MAT_lsmeans_8, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + 
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "MAT Score", title = "Mental Alteration Test Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

9.3.4) Planned contrasts

lsmeans.MAT8 <- lsmeans(modelMAT_8, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.MAT8,list(c1st,c2nd,c3rd),by=NULL)

##  contrast                                            estimate         SE  df
##  structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L))  0.4399710 0.08757185 Inf
##  structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) -0.5032525 0.09036545 Inf
##  structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) -0.0632814 0.08785589 Inf
##  z.ratio p.value
##    5.024  <.0001
##   -5.569  <.0001
##   -0.720  0.4713
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

9.4) Animal Fluency

9.4.1) Model

modelAnimals_8<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= Tracking.data_long)
summary(modelAnimals_8)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## Animal_Fluency_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 116933.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.3609 -0.5630 -0.0152  0.5500  4.7486 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.913    2.217   
##  Residual             3.633    1.906   
## Number of obs: 25010, groups:  ID, 8467
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             9.074e+00  3.740e-01
## timefactor2                                            -1.428e-01  4.377e-02
## timefactor3                                            -5.163e-02  4.426e-02
## PandemicFU2 data collected before COVID-19              1.729e-01  6.498e-02
## Age                                                    -1.770e-03  3.552e-03
## SexM                                                   -3.122e-01  6.067e-02
## EducationHigh School Diploma                            2.966e-01  8.542e-02
## EducationLess than High School Diploma                  4.156e-01  1.205e-01
## EducationSome College                                   2.496e-01  1.063e-01
## EthnicityWhite                                          1.308e+00  1.694e-01
## IncomeLevel>$150k                                       6.304e-01  1.586e-01
## IncomeLevel$100-150k                                    5.297e-01  1.268e-01
## IncomeLevel$20-50k                                      8.052e-02  8.438e-02
## IncomeLevel$50-100k                                     3.436e-01  9.092e-02
## BMI                                                    -1.565e-02  5.733e-03
## CESD.20.1                                              -3.527e-02  6.822e-03
## SmokingStatusFormer Smoker                              2.008e-01  1.164e-01
## SmokingStatusNever Smoked                               2.082e-01  1.211e-01
## SmokingStatusOccasional Smoker                          2.770e-01  2.295e-01
## RelationshipstatusMarried                              -1.813e-01  9.922e-02
## RelationshipstatusSeparated                            -1.565e-01  1.952e-01
## RelationshipstatusSingle                               -7.962e-02  1.340e-01
## RelationshipstatusWidowed                              -2.110e-01  1.354e-01
## LivingstatusAssisted Living                            -1.183e-01  4.018e-01
## LivingstatusHouse                                       3.409e-01  8.937e-02
## LivingstatusOther                                       1.257e-01  3.334e-01
## AnxietyYes                                             -7.203e-02  1.192e-01
## MoodDisordYes                                           2.103e-01  8.649e-02
## Chronicconditions                                      -1.593e-02  1.398e-02
## PASE_TOTALbaseline                                      9.462e-04  4.068e-04
## timefactor2:PandemicFU2 data collected before COVID-19  5.329e-02  5.918e-02
## timefactor3:PandemicFU2 data collected before COVID-19  7.531e-02  5.954e-02
##                                                                df t value
## (Intercept)                                             8.512e+03  24.266
## timefactor2                                             1.658e+04  -3.262
## timefactor3                                             1.665e+04  -1.166
## PandemicFU2 data collected before COVID-19              1.482e+04   2.660
## Age                                                     8.432e+03  -0.498
## SexM                                                    8.436e+03  -5.145
## EducationHigh School Diploma                            8.447e+03   3.473
## EducationLess than High School Diploma                  8.470e+03   3.449
## EducationSome College                                   8.421e+03   2.348
## EthnicityWhite                                          8.417e+03   7.722
## IncomeLevel>$150k                                       8.436e+03   3.974
## IncomeLevel$100-150k                                    8.427e+03   4.177
## IncomeLevel$20-50k                                      8.439e+03   0.954
## IncomeLevel$50-100k                                     8.439e+03   3.779
## BMI                                                     8.428e+03  -2.729
## CESD.20.1                                               8.441e+03  -5.170
## SmokingStatusFormer Smoker                              8.438e+03   1.725
## SmokingStatusNever Smoked                               8.436e+03   1.720
## SmokingStatusOccasional Smoker                          8.426e+03   1.207
## RelationshipstatusMarried                               8.454e+03  -1.827
## RelationshipstatusSeparated                             8.461e+03  -0.802
## RelationshipstatusSingle                                8.446e+03  -0.594
## RelationshipstatusWidowed                               8.440e+03  -1.559
## LivingstatusAssisted Living                             8.441e+03  -0.295
## LivingstatusHouse                                       8.450e+03   3.815
## LivingstatusOther                                       8.376e+03   0.377
## AnxietyYes                                              8.437e+03  -0.604
## MoodDisordYes                                           8.440e+03   2.431
## Chronicconditions                                       8.426e+03  -1.140
## PASE_TOTALbaseline                                      8.430e+03   2.326
## timefactor2:PandemicFU2 data collected before COVID-19  1.658e+04   0.900
## timefactor3:PandemicFU2 data collected before COVID-19  1.662e+04   1.265
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            0.001108 ** 
## timefactor3                                            0.243445    
## PandemicFU2 data collected before COVID-19             0.007818 ** 
## Age                                                    0.618234    
## SexM                                                   2.74e-07 ***
## EducationHigh School Diploma                           0.000518 ***
## EducationLess than High School Diploma                 0.000566 ***
## EducationSome College                                  0.018897 *  
## EthnicityWhite                                         1.27e-14 ***
## IncomeLevel>$150k                                      7.12e-05 ***
## IncomeLevel$100-150k                                   2.99e-05 ***
## IncomeLevel$20-50k                                     0.339992    
## IncomeLevel$50-100k                                    0.000159 ***
## BMI                                                    0.006359 ** 
## CESD.20.1                                              2.39e-07 ***
## SmokingStatusFormer Smoker                             0.084541 .  
## SmokingStatusNever Smoked                              0.085502 .  
## SmokingStatusOccasional Smoker                         0.227570    
## RelationshipstatusMarried                              0.067687 .  
## RelationshipstatusSeparated                            0.422670    
## RelationshipstatusSingle                               0.552441    
## RelationshipstatusWidowed                              0.119095    
## LivingstatusAssisted Living                            0.768355    
## LivingstatusHouse                                      0.000137 ***
## LivingstatusOther                                      0.706144    
## AnxietyYes                                             0.545818    
## MoodDisordYes                                          0.015064 *  
## Chronicconditions                                      0.254359    
## PASE_TOTALbaseline                                     0.020055 *  
## timefactor2:PandemicFU2 data collected before COVID-19 0.367881    
## timefactor3:PandemicFU2 data collected before COVID-19 0.205960    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

9.4.2) Estimated marginal means

lsmeans(modelAnimals_8, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  9.745959 0.1828005 Inf  9.387677 10.104242
##  FU2 data collected before COVID-19 9.918814 0.1829353 Inf  9.560268 10.277361
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  9.603186 0.1828656 Inf  9.244775  9.961596
##  FU2 data collected before COVID-19 9.829329 0.1829668 Inf  9.470720 10.187937
## 
## timefactor = 3:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  9.694331 0.1829621 Inf  9.335732 10.052930
##  FU2 data collected before COVID-19 9.942493 0.1829846 Inf  9.583850 10.301137
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelAnimals_8, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1728549 0.06497816 Inf  -2.660  0.0078
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.2261432 0.06527312 Inf  -3.465  0.0005
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.2481626 0.06562061 Inf  -3.782  0.0002
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelAnimals_8, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.1728549 0.06497816 Inf -0.3002097 -0.04550002
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.2261432 0.06527312 Inf -0.3540762 -0.09821027
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.2481626 0.06562061 Inf -0.3767766 -0.11954856
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

9.4.3) Graph of estimated marginal means

Animals_lsmeans_8 <- summary(lsmeans(modelAnimals_8, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.

Animals_lsmeans_8$Time<-NA
Animals_lsmeans_8$Time[Animals_lsmeans_8$timefactor==1]<-"Baseline"
Animals_lsmeans_8$Time[Animals_lsmeans_8$timefactor==2]<-"Follow-up 1"
Animals_lsmeans_8$Time[Animals_lsmeans_8$timefactor==3]<-"Follow-up 2"
ggplot(Animals_lsmeans_8, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + 
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "Animal Fluency (words)", title = "Animal Fluency Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

9.4.4) Planned contrasts

lsmeans.Animals8 <- lsmeans(modelAnimals_8, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.Animals8,list(c1st,c2nd,c3rd),by=NULL)

##  contrast                                             estimate         SE  df
##  structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) -0.05328836 0.05917816 Inf
##  structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) -0.02201937 0.05985472 Inf
##  structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) -0.07530773 0.05954112 Inf
##  z.ratio p.value
##   -0.900  0.3679
##   -0.368  0.7130
##   -1.265  0.2059
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

10) Full Adjusted Model: Time x Pandemic effects on cognition and physical activity ( adjusting for baseline)

All models use normalized cognitive scores. Each model is adjusted for baseline age, sex, education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, baseline PASE score, and baseline cognitive performance

10.1) RVLT Immediate Recall

10.1.1) Model

modelRVLT_imm_adj10<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +
                            (1|ID), data= Tracking.data_long_2)
summary(modelRVLT_imm_adj10)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## RVLT_Immediate_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +  
##     (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 85610
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.7099 -0.5660 -0.0356  0.5277  3.8353 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.089    2.022   
##  Residual             7.350    2.711   
## Number of obs: 16420, groups:  ID, 8448
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             7.454e+00  4.279e-01
## timefactor2                                             3.108e-01  6.352e-02
## PandemicFU2 data collected before COVID-19              1.794e-01  7.598e-02
## Age                                                    -1.474e-02  4.052e-03
## SexM                                                   -5.715e-01  6.915e-02
## EducationHigh School Diploma                            2.724e-01  9.700e-02
## EducationLess than High School Diploma                  5.141e-01  1.374e-01
## EducationSome College                                   2.371e-01  1.205e-01
## EthnicityWhite                                          4.852e-01  1.922e-01
## IncomeLevel>$150k                                       4.738e-01  1.804e-01
## IncomeLevel$100-150k                                    4.078e-01  1.439e-01
## IncomeLevel$20-50k                                      1.463e-01  9.587e-02
## IncomeLevel$50-100k                                     4.242e-01  1.033e-01
## BMI                                                    -1.687e-02  6.507e-03
## CESD.10baseline                                        -2.594e-02  7.747e-03
## SmokingStatusFormer Smoker                              1.758e-01  1.321e-01
## SmokingStatusNever Smoked                               2.787e-01  1.375e-01
## SmokingStatusOccasional Smoker                         -2.838e-02  2.599e-01
## RelationshipstatusMarried                               3.161e-01  1.127e-01
## RelationshipstatusSeparated                             2.365e-01  2.221e-01
## RelationshipstatusSingle                                1.878e-01  1.522e-01
## RelationshipstatusWidowed                               1.763e-02  1.539e-01
## LivingstatusAssisted Living                            -8.328e-01  4.570e-01
## LivingstatusHouse                                       4.362e-02  1.015e-01
## LivingstatusOther                                      -3.584e-01  3.776e-01
## AnxietyYes                                              6.747e-02  1.354e-01
## MoodDisordYes                                          -1.642e-01  9.819e-02
## Chronicconditions                                      -2.306e-02  1.587e-02
## PASE_TOTALbaseline                                      2.277e-03  4.624e-04
## RVLT_Immediate_Normedbaseline                           3.512e-01  8.160e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -8.311e-02  8.553e-02
##                                                                df t value
## (Intercept)                                             8.446e+03  17.419
## timefactor2                                             8.281e+03   4.893
## PandemicFU2 data collected before COVID-19              1.448e+04   2.361
## Age                                                     8.385e+03  -3.638
## SexM                                                    8.376e+03  -8.264
## EducationHigh School Diploma                            8.375e+03   2.808
## EducationLess than High School Diploma                  8.502e+03   3.741
## EducationSome College                                   8.296e+03   1.967
## EthnicityWhite                                          8.291e+03   2.524
## IncomeLevel>$150k                                       8.371e+03   2.627
## IncomeLevel$100-150k                                    8.322e+03   2.834
## IncomeLevel$20-50k                                      8.379e+03   1.526
## IncomeLevel$50-100k                                     8.375e+03   4.105
## BMI                                                     8.345e+03  -2.593
## CESD.10baseline                                         8.375e+03  -3.348
## SmokingStatusFormer Smoker                              8.393e+03   1.330
## SmokingStatusNever Smoked                               8.392e+03   2.027
## SmokingStatusOccasional Smoker                          8.265e+03  -0.109
## RelationshipstatusMarried                               8.393e+03   2.804
## RelationshipstatusSeparated                             8.461e+03   1.065
## RelationshipstatusSingle                                8.382e+03   1.234
## RelationshipstatusWidowed                               8.398e+03   0.115
## LivingstatusAssisted Living                             8.435e+03  -1.823
## LivingstatusHouse                                       8.374e+03   0.430
## LivingstatusOther                                       8.287e+03  -0.949
## AnxietyYes                                              8.368e+03   0.498
## MoodDisordYes                                           8.367e+03  -1.672
## Chronicconditions                                       8.362e+03  -1.453
## PASE_TOTALbaseline                                      8.367e+03   4.924
## RVLT_Immediate_Normedbaseline                           8.388e+03  43.034
## timefactor2:PandemicFU2 data collected before COVID-19  8.230e+03  -0.972
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            1.01e-06 ***
## PandemicFU2 data collected before COVID-19             0.018254 *  
## Age                                                    0.000276 ***
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                           0.004991 ** 
## EducationLess than High School Diploma                 0.000184 ***
## EducationSome College                                  0.049221 *  
## EthnicityWhite                                         0.011623 *  
## IncomeLevel>$150k                                      0.008637 ** 
## IncomeLevel$100-150k                                   0.004601 ** 
## IncomeLevel$20-50k                                     0.127043    
## IncomeLevel$50-100k                                    4.08e-05 ***
## BMI                                                    0.009525 ** 
## CESD.10baseline                                        0.000817 ***
## SmokingStatusFormer Smoker                             0.183503    
## SmokingStatusNever Smoked                              0.042735 *  
## SmokingStatusOccasional Smoker                         0.913045    
## RelationshipstatusMarried                              0.005054 ** 
## RelationshipstatusSeparated                            0.286915    
## RelationshipstatusSingle                               0.217165    
## RelationshipstatusWidowed                              0.908794    
## LivingstatusAssisted Living                            0.068413 .  
## LivingstatusHouse                                      0.667408    
## LivingstatusOther                                      0.342513    
## AnxietyYes                                             0.618158    
## MoodDisordYes                                          0.094596 .  
## Chronicconditions                                      0.146264    
## PASE_TOTALbaseline                                     8.63e-07 ***
## RVLT_Immediate_Normedbaseline                           < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.331232    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

10.1.2) Estimated marginal means

lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.40541 0.2077597 Inf  9.998208  10.81261
##  FU2 data collected before COVID-19 10.58478 0.2078880 Inf 10.177330  10.99224
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.71623 0.2079816 Inf 10.308597  11.12387
##  FU2 data collected before COVID-19 10.81250 0.2077872 Inf 10.405241  11.21975
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.17937348 0.07598370 Inf  -2.361  0.0182
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.09626301 0.07655159 Inf  -1.257  0.2086
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.17937348 0.07598370 Inf -0.3282988 -0.03044816
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.09626301 0.07655159 Inf -0.2463014  0.05377536
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

10.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_adj10 <- summary(lsmeans(modelRVLT_imm_adj10, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTimmediate_lsmeans_adj10$Time<-NA
RVLTimmediate_lsmeans_adj10$Time[RVLTimmediate_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj10$Time[RVLTimmediate_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

10.1.4) Planned contrasts

lsmeans.RVLTImm10 <- lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.RVLTImm10,list(c4th),by=NULL)

##  contrast                                      estimate         SE  df z.ratio
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.08311047 0.08553164 Inf   0.972
##  p.value
##   0.3312
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

10.2) RVLT Delayed Recall

10.2.1) Model

modelRVLT_del_adj10<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +
                            (1|ID), data= Tracking.data_long_2)
summary(modelRVLT_del_adj10)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +  
##     (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 84249.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8988 -0.5550 -0.0364  0.5114  4.0165 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.115    2.029   
##  Residual             6.942    2.635   
## Number of obs: 16282, groups:  ID, 8435
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             6.568e+00  4.255e-01
## timefactor2                                             5.852e-01  6.217e-02
## PandemicFU2 data collected before COVID-19              2.664e-01  7.498e-02
## Age                                                    -6.290e-03  4.024e-03
## SexM                                                   -5.303e-01  6.846e-02
## EducationHigh School Diploma                            2.956e-01  9.617e-02
## EducationLess than High School Diploma                  3.886e-01  1.367e-01
## EducationSome College                                   3.012e-01  1.194e-01
## EthnicityWhite                                          7.048e-01  1.916e-01
## IncomeLevel>$150k                                       4.192e-01  1.785e-01
## IncomeLevel$100-150k                                    3.324e-01  1.426e-01
## IncomeLevel$20-50k                                      1.598e-01  9.505e-02
## IncomeLevel$50-100k                                     4.515e-01  1.024e-01
## BMI                                                    -1.834e-02  6.446e-03
## CESD.10baseline                                        -2.162e-02  7.687e-03
## SmokingStatusFormer Smoker                              9.409e-02  1.308e-01
## SmokingStatusNever Smoked                               2.694e-01  1.361e-01
## SmokingStatusOccasional Smoker                          6.592e-02  2.578e-01
## RelationshipstatusMarried                               8.316e-02  1.118e-01
## RelationshipstatusSeparated                            -5.816e-02  2.203e-01
## RelationshipstatusSingle                                2.294e-02  1.508e-01
## RelationshipstatusWidowed                              -1.445e-01  1.526e-01
## LivingstatusAssisted Living                            -1.047e+00  4.534e-01
## LivingstatusHouse                                       1.008e-01  1.007e-01
## LivingstatusOther                                      -2.094e-01  3.751e-01
## AnxietyYes                                              1.046e-01  1.341e-01
## MoodDisordYes                                          -1.729e-01  9.725e-02
## Chronicconditions                                      -3.090e-02  1.573e-02
## PASE_TOTALbaseline                                      2.308e-03  4.584e-04
## RVLT_Delayed_Normedbaseline                             3.921e-01  8.304e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -2.805e-01  8.362e-02
##                                                                df t value
## (Intercept)                                             8.471e+03  15.437
## timefactor2                                             8.230e+03   9.413
## PandemicFU2 data collected before COVID-19              1.430e+04   3.553
## Age                                                     8.386e+03  -1.563
## SexM                                                    8.357e+03  -7.746
## EducationHigh School Diploma                            8.359e+03   3.074
## EducationLess than High School Diploma                  8.552e+03   2.843
## EducationSome College                                   8.276e+03   2.523
## EthnicityWhite                                          8.393e+03   3.678
## IncomeLevel>$150k                                       8.347e+03   2.348
## IncomeLevel$100-150k                                    8.319e+03   2.331
## IncomeLevel$20-50k                                      8.379e+03   1.681
## IncomeLevel$50-100k                                     8.359e+03   4.409
## BMI                                                     8.319e+03  -2.844
## CESD.10baseline                                         8.388e+03  -2.813
## SmokingStatusFormer Smoker                              8.369e+03   0.719
## SmokingStatusNever Smoked                               8.366e+03   1.979
## SmokingStatusOccasional Smoker                          8.288e+03   0.256
## RelationshipstatusMarried                               8.349e+03   0.744
## RelationshipstatusSeparated                             8.470e+03  -0.264
## RelationshipstatusSingle                                8.335e+03   0.152
## RelationshipstatusWidowed                               8.369e+03  -0.947
## LivingstatusAssisted Living                             8.465e+03  -2.308
## LivingstatusHouse                                       8.376e+03   1.001
## LivingstatusOther                                       8.341e+03  -0.558
## AnxietyYes                                              8.338e+03   0.780
## MoodDisordYes                                           8.344e+03  -1.778
## Chronicconditions                                       8.348e+03  -1.964
## PASE_TOTALbaseline                                      8.358e+03   5.034
## RVLT_Delayed_Normedbaseline                             8.363e+03  47.224
## timefactor2:PandemicFU2 data collected before COVID-19  8.170e+03  -3.355
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             0.000382 ***
## Age                                                    0.118036    
## SexM                                                   1.06e-14 ***
## EducationHigh School Diploma                           0.002121 ** 
## EducationLess than High School Diploma                 0.004482 ** 
## EducationSome College                                  0.011644 *  
## EthnicityWhite                                         0.000237 ***
## IncomeLevel>$150k                                      0.018884 *  
## IncomeLevel$100-150k                                   0.019797 *  
## IncomeLevel$20-50k                                     0.092806 .  
## IncomeLevel$50-100k                                    1.05e-05 ***
## BMI                                                    0.004460 ** 
## CESD.10baseline                                        0.004917 ** 
## SmokingStatusFormer Smoker                             0.472075    
## SmokingStatusNever Smoked                              0.047843 *  
## SmokingStatusOccasional Smoker                         0.798193    
## RelationshipstatusMarried                              0.456884    
## RelationshipstatusSeparated                            0.791753    
## RelationshipstatusSingle                               0.879086    
## RelationshipstatusWidowed                              0.343720    
## LivingstatusAssisted Living                            0.020999 *  
## LivingstatusHouse                                      0.316968    
## LivingstatusOther                                      0.576638    
## AnxietyYes                                             0.435501    
## MoodDisordYes                                          0.075487 .  
## Chronicconditions                                      0.049550 *  
## PASE_TOTALbaseline                                     4.89e-07 ***
## RVLT_Delayed_Normedbaseline                             < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.000798 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

10.2.2) Estimated marginal means

lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.48443 0.2062365 Inf  10.08022  10.88865
##  FU2 data collected before COVID-19 10.75083 0.2063549 Inf  10.34639  11.15528
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.06962 0.2064417 Inf  10.66500  11.47423
##  FU2 data collected before COVID-19 11.05548 0.2062749 Inf  10.65119  11.45978
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.26640234 0.07498067 Inf  -3.553  0.0004
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##   0.01413065 0.07552241 Inf   0.187  0.8516
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL  asymp.UCL
##  -0.26640234 0.07498067 Inf -0.4133618 -0.1194429
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL  asymp.UCL
##   0.01413065 0.07552241 Inf -0.1338905  0.1621518
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

10.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_adj10 <- summary(lsmeans(modelRVLT_del_adj10, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTdelayed_lsmeans_adj10$Time<-NA
RVLTdelayed_lsmeans_adj10$Time[RVLTdelayed_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj10$Time[RVLTdelayed_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

10.2.4) Planned contrasts

lsmeans.RVLTDel10 <- lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.RVLTDel10,list(c4th),by=NULL)

##  contrast                                    estimate         SE  df z.ratio
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.280533 0.08362461 Inf   3.355
##  p.value
##   0.0008
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

10.3) Mental Alteration Test

10.3.1) Model

modelMAT_adj10<- lmer(MAT_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + MAT_Normedbaseline +
                      (1|ID), data= Tracking.data_long_2)
summary(modelMAT_adj10)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + MAT_Normedbaseline +      (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 79636.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4748 -0.5205 -0.0783  0.3697  4.9556 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.353    1.534   
##  Residual             8.304    2.882   
## Number of obs: 15353, groups:  ID, 8344
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             7.636e+00  4.088e-01
## timefactor2                                            -1.365e+00  7.034e-02
## PandemicFU2 data collected before COVID-19             -4.697e-01  7.582e-02
## Age                                                    -1.255e-02  3.827e-03
## SexM                                                   -1.093e+00  6.501e-02
## EducationHigh School Diploma                            3.828e-03  9.152e-02
## EducationLess than High School Diploma                 -5.448e-02  1.317e-01
## EducationSome College                                  -8.197e-02  1.129e-01
## EthnicityWhite                                          8.887e-01  1.833e-01
## IncomeLevel>$150k                                       2.230e-01  1.699e-01
## IncomeLevel$100-150k                                    2.581e-01  1.356e-01
## IncomeLevel$20-50k                                      1.562e-01  9.074e-02
## IncomeLevel$50-100k                                     1.873e-01  9.781e-02
## BMI                                                    -1.367e-02  6.105e-03
## CESD.10baseline                                        -1.840e-02  7.302e-03
## SmokingStatusFormer Smoker                              9.113e-02  1.241e-01
## SmokingStatusNever Smoked                               1.816e-02  1.291e-01
## SmokingStatusOccasional Smoker                          1.809e-01  2.445e-01
## RelationshipstatusMarried                               2.547e-02  1.064e-01
## RelationshipstatusSeparated                            -5.661e-02  2.089e-01
## RelationshipstatusSingle                                5.263e-01  1.434e-01
## RelationshipstatusWidowed                              -8.460e-02  1.459e-01
## LivingstatusAssisted Living                            -3.059e-01  4.319e-01
## LivingstatusHouse                                      -2.065e-01  9.608e-02
## LivingstatusOther                                      -3.766e-01  3.588e-01
## AnxietyYes                                              6.174e-02  1.271e-01
## MoodDisordYes                                           1.262e-01  9.213e-02
## Chronicconditions                                      -3.402e-02  1.503e-02
## PASE_TOTALbaseline                                     -6.223e-04  4.339e-04
## MAT_Normedbaseline                                      4.490e-01  8.549e-03
## timefactor2:PandemicFU2 data collected before COVID-19  5.097e-01  9.449e-02
##                                                                df t value
## (Intercept)                                             8.221e+03  18.676
## timefactor2                                             7.889e+03 -19.411
## PandemicFU2 data collected before COVID-19              1.472e+04  -6.195
## Age                                                     8.180e+03  -3.278
## SexM                                                    8.078e+03 -16.805
## EducationHigh School Diploma                            8.077e+03   0.042
## EducationLess than High School Diploma                  8.326e+03  -0.414
## EducationSome College                                   7.966e+03  -0.726
## EthnicityWhite                                          8.203e+03   4.849
## IncomeLevel>$150k                                       8.064e+03   1.313
## IncomeLevel$100-150k                                    8.039e+03   1.903
## IncomeLevel$20-50k                                      8.141e+03   1.722
## IncomeLevel$50-100k                                     8.095e+03   1.915
## BMI                                                     7.968e+03  -2.239
## CESD.10baseline                                         8.039e+03  -2.520
## SmokingStatusFormer Smoker                              8.022e+03   0.735
## SmokingStatusNever Smoked                               8.023e+03   0.141
## SmokingStatusOccasional Smoker                          7.892e+03   0.740
## RelationshipstatusMarried                               8.133e+03   0.239
## RelationshipstatusSeparated                             8.186e+03  -0.271
## RelationshipstatusSingle                                8.103e+03   3.670
## RelationshipstatusWidowed                               8.186e+03  -0.580
## LivingstatusAssisted Living                             8.064e+03  -0.708
## LivingstatusHouse                                       8.146e+03  -2.150
## LivingstatusOther                                       8.143e+03  -1.049
## AnxietyYes                                              8.045e+03   0.486
## MoodDisordYes                                           8.017e+03   1.370
## Chronicconditions                                       8.136e+03  -2.264
## PASE_TOTALbaseline                                      8.031e+03  -1.434
## MAT_Normedbaseline                                      8.135e+03  52.521
## timefactor2:PandemicFU2 data collected before COVID-19  7.827e+03   5.394
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             5.97e-10 ***
## Age                                                    0.001048 ** 
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                           0.966640    
## EducationLess than High School Diploma                 0.679223    
## EducationSome College                                  0.467668    
## EthnicityWhite                                         1.26e-06 ***
## IncomeLevel>$150k                                      0.189326    
## IncomeLevel$100-150k                                   0.057013 .  
## IncomeLevel$20-50k                                     0.085189 .  
## IncomeLevel$50-100k                                    0.055552 .  
## BMI                                                    0.025155 *  
## CESD.10baseline                                        0.011742 *  
## SmokingStatusFormer Smoker                             0.462610    
## SmokingStatusNever Smoked                              0.888127    
## SmokingStatusOccasional Smoker                         0.459449    
## RelationshipstatusMarried                              0.810900    
## RelationshipstatusSeparated                            0.786349    
## RelationshipstatusSingle                               0.000244 ***
## RelationshipstatusWidowed                              0.562045    
## LivingstatusAssisted Living                            0.478757    
## LivingstatusHouse                                      0.031622 *  
## LivingstatusOther                                      0.293997    
## AnxietyYes                                             0.627040    
## MoodDisordYes                                          0.170674    
## Chronicconditions                                      0.023595 *  
## PASE_TOTALbaseline                                     0.151566    
## MAT_Normedbaseline                                      < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 7.08e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

10.3.2) Estimated marginal means

lsmeans(modelMAT_adj10, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.806895 0.1977972 Inf 10.419220 11.194571
##  FU2 data collected before COVID-19 10.337165 0.1975870 Inf  9.949902 10.724429
## 
## timefactor = 2:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.441577 0.1975127 Inf  9.054459  9.828695
##  FU2 data collected before COVID-19  9.481585 0.1974365 Inf  9.094617  9.868554
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelMAT_adj10, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##   0.4697302 0.07582012 Inf   6.195  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.0400084 0.07615539 Inf  -0.525  0.5993
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelMAT_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL asymp.UCL
##   0.4697302 0.07582012 Inf  0.3211255 0.6183349
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL asymp.UCL
##  -0.0400084 0.07615539 Inf -0.1892702 0.1092535
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

10.3.3) Graph of estimated marginal means

MAT_lsmeans_adj10 <- summary(lsmeans(modelMAT_adj10, ~Pandemic|timefactor))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.

MAT_lsmeans_adj10$Time<-NA
MAT_lsmeans_adj10$Time[MAT_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj10$Time[MAT_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

10.3.4) Planned contrasts

lsmeans.MAT10 <- lsmeans(modelMAT_adj10, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.MAT10,list(c4th),by=NULL)

##  contrast                                      estimate         SE  df z.ratio
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.5097385 0.09449385 Inf  -5.394
##  p.value
##   <.0001
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

10.4) Animal Fluency

10.4.1) Model

modelAnimals_adj10<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +
                           (1|ID), data= Tracking.data_long_2)
summary(modelAnimals_adj10)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## Animal_Fluency_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +  
##     (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 73835.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8973 -0.5565 -0.0188  0.5329  4.7771 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.379    1.543   
##  Residual             3.191    1.786   
## Number of obs: 16543, groups:  ID, 8454
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             5.080e+00  3.085e-01
## timefactor2                                             9.181e-02  4.176e-02
## PandemicFU2 data collected before COVID-19              1.678e-01  5.281e-02
## Age                                                    -1.415e-02  2.875e-03
## SexM                                                   -1.683e-01  4.917e-02
## EducationHigh School Diploma                            2.332e-01  6.923e-02
## EducationLess than High School Diploma                  1.463e-01  9.784e-02
## EducationSome College                                   1.784e-01  8.597e-02
## EthnicityWhite                                          4.594e-01  1.374e-01
## IncomeLevel>$150k                                       3.605e-03  1.286e-01
## IncomeLevel$100-150k                                    4.776e-02  1.028e-01
## IncomeLevel$20-50k                                     -1.061e-01  6.835e-02
## IncomeLevel$50-100k                                     6.691e-02  7.369e-02
## BMI                                                    -9.107e-03  4.640e-03
## CESD.10baseline                                        -1.015e-02  5.533e-03
## SmokingStatusFormer Smoker                              1.414e-01  9.422e-02
## SmokingStatusNever Smoked                               1.664e-01  9.802e-02
## SmokingStatusOccasional Smoker                         -2.532e-02  1.858e-01
## RelationshipstatusMarried                               4.729e-02  8.046e-02
## RelationshipstatusSeparated                             1.877e-01  1.583e-01
## RelationshipstatusSingle                                5.719e-02  1.086e-01
## RelationshipstatusWidowed                              -1.872e-02  1.096e-01
## LivingstatusAssisted Living                            -2.391e-01  3.253e-01
## LivingstatusHouse                                       1.539e-01  7.248e-02
## LivingstatusOther                                      -2.822e-01  2.687e-01
## AnxietyYes                                              1.026e-01  9.656e-02
## MoodDisordYes                                           7.169e-02  7.006e-02
## Chronicconditions                                      -1.343e-02  1.131e-02
## PASE_TOTALbaseline                                      4.653e-04  3.293e-04
## Animal_Fluency_Normedbaseline                           5.275e-01  7.168e-03
## timefactor2:PandemicFU2 data collected before COVID-19  2.120e-02  5.614e-02
##                                                                df t value
## (Intercept)                                             8.450e+03  16.465
## timefactor2                                             8.321e+03   2.199
## PandemicFU2 data collected before COVID-19              1.389e+04   3.178
## Age                                                     8.378e+03  -4.920
## SexM                                                    8.380e+03  -3.424
## EducationHigh School Diploma                            8.392e+03   3.369
## EducationLess than High School Diploma                  8.481e+03   1.496
## EducationSome College                                   8.339e+03   2.075
## EthnicityWhite                                          8.311e+03   3.343
## IncomeLevel>$150k                                       8.389e+03   0.028
## IncomeLevel$100-150k                                    8.350e+03   0.465
## IncomeLevel$20-50k                                      8.383e+03  -1.552
## IncomeLevel$50-100k                                     8.381e+03   0.908
## BMI                                                     8.357e+03  -1.963
## CESD.10baseline                                         8.397e+03  -1.835
## SmokingStatusFormer Smoker                              8.396e+03   1.501
## SmokingStatusNever Smoked                               8.393e+03   1.698
## SmokingStatusOccasional Smoker                          8.321e+03  -0.136
## RelationshipstatusMarried                               8.417e+03   0.588
## RelationshipstatusSeparated                             8.454e+03   1.186
## RelationshipstatusSingle                                8.408e+03   0.527
## RelationshipstatusWidowed                               8.387e+03  -0.171
## LivingstatusAssisted Living                             8.405e+03  -0.735
## LivingstatusHouse                                       8.399e+03   2.123
## LivingstatusOther                                       8.242e+03  -1.050
## AnxietyYes                                              8.369e+03   1.063
## MoodDisordYes                                           8.389e+03   1.023
## Chronicconditions                                       8.356e+03  -1.188
## PASE_TOTALbaseline                                      8.370e+03   1.413
## Animal_Fluency_Normedbaseline                           8.364e+03  73.588
## timefactor2:PandemicFU2 data collected before COVID-19  8.263e+03   0.378
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            0.027920 *  
## PandemicFU2 data collected before COVID-19             0.001484 ** 
## Age                                                    8.84e-07 ***
## SexM                                                   0.000620 ***
## EducationHigh School Diploma                           0.000758 ***
## EducationLess than High School Diploma                 0.134805    
## EducationSome College                                  0.038002 *  
## EthnicityWhite                                         0.000833 ***
## IncomeLevel>$150k                                      0.977631    
## IncomeLevel$100-150k                                   0.642151    
## IncomeLevel$20-50k                                     0.120713    
## IncomeLevel$50-100k                                    0.363888    
## BMI                                                    0.049706 *  
## CESD.10baseline                                        0.066550 .  
## SmokingStatusFormer Smoker                             0.133337    
## SmokingStatusNever Smoked                              0.089526 .  
## SmokingStatusOccasional Smoker                         0.891580    
## RelationshipstatusMarried                              0.556703    
## RelationshipstatusSeparated                            0.235685    
## RelationshipstatusSingle                               0.598337    
## RelationshipstatusWidowed                              0.864439    
## LivingstatusAssisted Living                            0.462495    
## LivingstatusHouse                                      0.033777 *  
## LivingstatusOther                                      0.293663    
## AnxietyYes                                             0.287883    
## MoodDisordYes                                          0.306261    
## Chronicconditions                                      0.235037    
## PASE_TOTALbaseline                                     0.157657    
## Animal_Fluency_Normedbaseline                           < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.705696    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

10.4.2) Estimated marginal means

lsmeans(modelAnimals_adj10, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.832780 0.1478945 Inf  9.542912  10.12265
##  FU2 data collected before COVID-19 10.000617 0.1479464 Inf  9.710647  10.29059
## 
## timefactor = 2:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.924592 0.1479894 Inf  9.634538  10.21465
##  FU2 data collected before COVID-19 10.113629 0.1479770 Inf  9.823600  10.40366
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelAnimals_adj10, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1678372 0.05280608 Inf  -3.178  0.0015
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1890379 0.05321063 Inf  -3.553  0.0004
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelAnimals_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.1678372 0.05280608 Inf -0.2713352 -0.06433915
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.1890379 0.05321063 Inf -0.2933288 -0.08474695
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

10.4.3) Graph of estimated marginal means

Animals_lsmeans_adj10 <- summary(lsmeans(modelAnimals_adj10, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.

Animals_lsmeans_adj10$Time<-NA
Animals_lsmeans_adj10$Time[Animals_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj10$Time[Animals_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

10.3.4) Planned contrasts

lsmeans.Animals10 <- lsmeans(modelAnimals_adj10, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.Animals10,list(c4th),by=NULL)

##  contrast                                      estimate         SE  df z.ratio
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.0212007 0.05613757 Inf  -0.378
##  p.value
##   0.7057
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

11) Age and sex differences in time x pandemic effects (not adjusted for baseline; full model only)

All models use normalized cognitive scores.Each model is adjusted for baseline age, sex, education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, and baseline PASE score

emm_options(rg.limit = 350000)

Age and Sex grouping

Tracking.data_long$Age_sex<-NA
Tracking.data_long$Age_sex[Tracking.data_long$Age<=64 & Tracking.data_long$Sex == "M"]<-"Males 45-64"
Tracking.data_long$Age_sex[Tracking.data_long$Age<=64 & Tracking.data_long$Sex == "F"]<-"Females 45-64"
Tracking.data_long$Age_sex[Tracking.data_long$Age>64 & Tracking.data_long$Sex == "M"]<-"Males 65+"
Tracking.data_long$Age_sex[Tracking.data_long$Age>64 & Tracking.data_long$Sex == "F"]<-"Females 65+"

11.1) RVLT Immediate Recall

11.1.1) Model

modelRVLT_imm_11<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                         (1|ID), data= Tracking.data_long)
summary(modelRVLT_imm_11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Immediate_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 132211.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6817 -0.5799 -0.0467  0.5201  5.0159 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.253    2.292   
##  Residual             8.275    2.877   
## Number of obs: 24887, groups:  ID, 8467
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                8.763e+00
## timefactor2                                                                1.103e+00
## timefactor3                                                                1.510e+00
## PandemicFU2 data collected before COVID-19                                 6.651e-01
## Age_sexFemales 65+                                                         1.288e+00
## Age_sexMales 45-64                                                        -6.742e-01
## Age_sexMales 65+                                                           3.018e-01
## EducationHigh School Diploma                                               2.339e-01
## EducationLess than High School Diploma                                     7.360e-01
## EducationSome College                                                      3.613e-01
## EthnicityWhite                                                             9.758e-01
## IncomeLevel>$150k                                                          8.914e-01
## IncomeLevel$100-150k                                                       5.682e-01
## IncomeLevel$20-50k                                                         9.433e-02
## IncomeLevel$50-100k                                                        5.408e-01
## BMI                                                                       -1.984e-02
## CESD.20.1                                                                 -3.635e-02
## SmokingStatusFormer Smoker                                                 2.016e-01
## SmokingStatusNever Smoked                                                  3.643e-01
## SmokingStatusOccasional Smoker                                             2.828e-01
## RelationshipstatusMarried                                                  9.763e-02
## RelationshipstatusSeparated                                               -1.258e-01
## RelationshipstatusSingle                                                  -9.473e-02
## RelationshipstatusWidowed                                                 -7.085e-02
## LivingstatusAssisted Living                                               -5.967e-01
## LivingstatusHouse                                                          1.083e-01
## LivingstatusOther                                                         -3.506e-01
## AnxietyYes                                                                -3.487e-02
## MoodDisordYes                                                             -1.287e-01
## Chronicconditions                                                         -3.283e-02
## PASE_TOTALbaseline                                                         2.816e-03
## timefactor2:PandemicFU2 data collected before COVID-19                    -3.502e-01
## timefactor3:PandemicFU2 data collected before COVID-19                    -4.879e-01
## timefactor2:Age_sexFemales 65+                                            -1.060e+00
## timefactor3:Age_sexFemales 65+                                            -1.554e+00
## timefactor2:Age_sexMales 45-64                                            -2.463e-01
## timefactor3:Age_sexMales 45-64                                            -1.447e-01
## timefactor2:Age_sexMales 65+                                              -9.250e-01
## timefactor3:Age_sexMales 65+                                              -1.347e+00
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             -5.781e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.249e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               -4.640e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  5.078e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  5.834e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -7.213e-02
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.413e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    3.911e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    7.493e-01
##                                                                           Std. Error
## (Intercept)                                                                3.410e-01
## timefactor2                                                                1.237e-01
## timefactor3                                                                1.254e-01
## PandemicFU2 data collected before COVID-19                                 1.425e-01
## Age_sexFemales 65+                                                         2.007e-01
## Age_sexMales 45-64                                                         1.477e-01
## Age_sexMales 65+                                                           1.878e-01
## EducationHigh School Diploma                                               9.797e-02
## EducationLess than High School Diploma                                     1.384e-01
## EducationSome College                                                      1.218e-01
## EthnicityWhite                                                             1.941e-01
## IncomeLevel>$150k                                                          1.819e-01
## IncomeLevel$100-150k                                                       1.453e-01
## IncomeLevel$20-50k                                                         9.692e-02
## IncomeLevel$50-100k                                                        1.044e-01
## BMI                                                                        6.556e-03
## CESD.20.1                                                                  7.808e-03
## SmokingStatusFormer Smoker                                                 1.334e-01
## SmokingStatusNever Smoked                                                  1.389e-01
## SmokingStatusOccasional Smoker                                             2.629e-01
## RelationshipstatusMarried                                                  1.139e-01
## RelationshipstatusSeparated                                                2.239e-01
## RelationshipstatusSingle                                                   1.536e-01
## RelationshipstatusWidowed                                                  1.549e-01
## LivingstatusAssisted Living                                                4.610e-01
## LivingstatusHouse                                                          1.025e-01
## LivingstatusOther                                                          3.824e-01
## AnxietyYes                                                                 1.366e-01
## MoodDisordYes                                                              9.925e-02
## Chronicconditions                                                          1.587e-02
## PASE_TOTALbaseline                                                         4.518e-04
## timefactor2:PandemicFU2 data collected before COVID-19                     1.582e-01
## timefactor3:PandemicFU2 data collected before COVID-19                     1.596e-01
## timefactor2:Age_sexFemales 65+                                             2.147e-01
## timefactor3:Age_sexFemales 65+                                             2.178e-01
## timefactor2:Age_sexMales 45-64                                             1.614e-01
## timefactor3:Age_sexMales 45-64                                             1.629e-01
## timefactor2:Age_sexMales 65+                                               2.048e-01
## timefactor3:Age_sexMales 65+                                               2.081e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.459e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              2.022e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.390e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.733e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.759e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.251e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.260e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.662e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.686e-01
##                                                                                   df
## (Intercept)                                                                9.137e+03
## timefactor2                                                                1.647e+04
## timefactor3                                                                1.660e+04
## PandemicFU2 data collected before COVID-19                                 1.906e+04
## Age_sexFemales 65+                                                         1.809e+04
## Age_sexMales 45-64                                                         1.859e+04
## Age_sexMales 65+                                                           1.857e+04
## EducationHigh School Diploma                                               8.406e+03
## EducationLess than High School Diploma                                     8.467e+03
## EducationSome College                                                      8.369e+03
## EthnicityWhite                                                             8.364e+03
## IncomeLevel>$150k                                                          8.405e+03
## IncomeLevel$100-150k                                                       8.377e+03
## IncomeLevel$20-50k                                                         8.409e+03
## IncomeLevel$50-100k                                                        8.406e+03
## BMI                                                                        8.390e+03
## CESD.20.1                                                                  8.398e+03
## SmokingStatusFormer Smoker                                                 8.406e+03
## SmokingStatusNever Smoked                                                  8.406e+03
## SmokingStatusOccasional Smoker                                             8.354e+03
## RelationshipstatusMarried                                                  8.414e+03
## RelationshipstatusSeparated                                                8.444e+03
## RelationshipstatusSingle                                                   8.406e+03
## RelationshipstatusWidowed                                                  8.421e+03
## LivingstatusAssisted Living                                                8.429e+03
## LivingstatusHouse                                                          8.409e+03
## LivingstatusOther                                                          8.346e+03
## AnxietyYes                                                                 8.398e+03
## MoodDisordYes                                                              8.400e+03
## Chronicconditions                                                          8.397e+03
## PASE_TOTALbaseline                                                         8.392e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.646e+04
## timefactor3:PandemicFU2 data collected before COVID-19                     1.655e+04
## timefactor2:Age_sexFemales 65+                                             1.646e+04
## timefactor3:Age_sexFemales 65+                                             1.659e+04
## timefactor2:Age_sexMales 45-64                                             1.647e+04
## timefactor3:Age_sexMales 45-64                                             1.656e+04
## timefactor2:Age_sexMales 65+                                               1.647e+04
## timefactor3:Age_sexMales 65+                                               1.662e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.906e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.911e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.911e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.647e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.656e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.647e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.651e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.647e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.656e+04
##                                                                           t value
## (Intercept)                                                                25.694
## timefactor2                                                                 8.914
## timefactor3                                                                12.040
## PandemicFU2 data collected before COVID-19                                  4.668
## Age_sexFemales 65+                                                          6.419
## Age_sexMales 45-64                                                         -4.564
## Age_sexMales 65+                                                            1.607
## EducationHigh School Diploma                                                2.387
## EducationLess than High School Diploma                                      5.318
## EducationSome College                                                       2.966
## EthnicityWhite                                                              5.027
## IncomeLevel>$150k                                                           4.900
## IncomeLevel$100-150k                                                        3.912
## IncomeLevel$20-50k                                                          0.973
## IncomeLevel$50-100k                                                         5.181
## BMI                                                                        -3.025
## CESD.20.1                                                                  -4.655
## SmokingStatusFormer Smoker                                                  1.512
## SmokingStatusNever Smoked                                                   2.623
## SmokingStatusOccasional Smoker                                              1.076
## RelationshipstatusMarried                                                   0.857
## RelationshipstatusSeparated                                                -0.562
## RelationshipstatusSingle                                                   -0.617
## RelationshipstatusWidowed                                                  -0.457
## LivingstatusAssisted Living                                                -1.294
## LivingstatusHouse                                                           1.057
## LivingstatusOther                                                          -0.917
## AnxietyYes                                                                 -0.255
## MoodDisordYes                                                              -1.297
## Chronicconditions                                                          -2.068
## PASE_TOTALbaseline                                                          6.233
## timefactor2:PandemicFU2 data collected before COVID-19                     -2.213
## timefactor3:PandemicFU2 data collected before COVID-19                     -3.057
## timefactor2:Age_sexFemales 65+                                             -4.939
## timefactor3:Age_sexFemales 65+                                             -7.135
## timefactor2:Age_sexMales 45-64                                             -1.526
## timefactor3:Age_sexMales 45-64                                             -0.888
## timefactor2:Age_sexMales 65+                                               -4.516
## timefactor3:Age_sexMales 65+                                               -6.475
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              -2.351
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.618
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -1.941
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   1.858
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   2.114
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -0.320
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   0.107
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.469
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     2.790
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                                < 2e-16
## timefactor3                                                                < 2e-16
## PandemicFU2 data collected before COVID-19                                3.06e-06
## Age_sexFemales 65+                                                        1.40e-10
## Age_sexMales 45-64                                                        5.06e-06
## Age_sexMales 65+                                                           0.10797
## EducationHigh School Diploma                                               0.01701
## EducationLess than High School Diploma                                    1.07e-07
## EducationSome College                                                      0.00303
## EthnicityWhite                                                            5.08e-07
## IncomeLevel>$150k                                                         9.78e-07
## IncomeLevel$100-150k                                                      9.24e-05
## IncomeLevel$20-50k                                                         0.33043
## IncomeLevel$50-100k                                                       2.26e-07
## BMI                                                                        0.00249
## CESD.20.1                                                                 3.28e-06
## SmokingStatusFormer Smoker                                                 0.13062
## SmokingStatusNever Smoked                                                  0.00873
## SmokingStatusOccasional Smoker                                             0.28206
## RelationshipstatusMarried                                                  0.39148
## RelationshipstatusSeparated                                                0.57429
## RelationshipstatusSingle                                                   0.53741
## RelationshipstatusWidowed                                                  0.64741
## LivingstatusAssisted Living                                                0.19555
## LivingstatusHouse                                                          0.29076
## LivingstatusOther                                                          0.35918
## AnxietyYes                                                                 0.79856
## MoodDisordYes                                                              0.19483
## Chronicconditions                                                          0.03864
## PASE_TOTALbaseline                                                        4.80e-10
## timefactor2:PandemicFU2 data collected before COVID-19                     0.02688
## timefactor3:PandemicFU2 data collected before COVID-19                     0.00224
## timefactor2:Age_sexFemales 65+                                            7.93e-07
## timefactor3:Age_sexFemales 65+                                            1.01e-12
## timefactor2:Age_sexMales 45-64                                             0.12698
## timefactor3:Age_sexMales 45-64                                             0.37442
## timefactor2:Age_sexMales 65+                                              6.35e-06
## timefactor3:Age_sexMales 65+                                              9.77e-11
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              0.01875
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              0.53685
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                0.05225
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  0.06316
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  0.03450
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  0.74863
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  0.91497
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    0.14183
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    0.00528
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## timefactor3                                                               ***
## PandemicFU2 data collected before COVID-19                                ***
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                             
## EducationHigh School Diploma                                              *  
## EducationLess than High School Diploma                                    ***
## EducationSome College                                                     ** 
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                         ***
## IncomeLevel$100-150k                                                      ***
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       ** 
## CESD.20.1                                                                 ***
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                 ** 
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                            
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         *  
## PASE_TOTALbaseline                                                        ***
## timefactor2:PandemicFU2 data collected before COVID-19                    *  
## timefactor3:PandemicFU2 data collected before COVID-19                    ** 
## timefactor2:Age_sexFemales 65+                                            ***
## timefactor3:Age_sexFemales 65+                                            ***
## timefactor2:Age_sexMales 45-64                                               
## timefactor3:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                              ***
## timefactor3:Age_sexMales 65+                                              ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             *  
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               .  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ .  
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ *  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

11.1.2) Estimated marginal means

Significant differences for males 45-64 and 65+, whereby post-pandemic males performed worse

lsmeans(modelRVLT_imm_11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.561568 0.2322923 Inf  9.106283 10.016852
##  FU2 data collected before COVID-19 10.226675 0.2232793 Inf  9.789056 10.664294
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.664144 0.2328179 Inf 10.207829 11.120459
##  FU2 data collected before COVID-19 10.979022 0.2235560 Inf 10.540861 11.417184
## 
## timefactor = 3, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.071726 0.2336883 Inf 10.613705 11.529746
##  FU2 data collected before COVID-19 11.248962 0.2235464 Inf 10.810819 11.687104
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.849650 0.2593162 Inf 10.341399 11.357900
##  FU2 data collected before COVID-19 10.936676 0.2424978 Inf 10.461389 11.411963
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.891887 0.2599483 Inf 10.382398 11.401377
##  FU2 data collected before COVID-19 11.136442 0.2434508 Inf 10.659287 11.613597
## 
## timefactor = 3, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.805592 0.2617093 Inf 10.292651 11.318533
##  FU2 data collected before COVID-19 10.988098 0.2434786 Inf 10.510889 11.465307
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   8.887409 0.2229581 Inf  8.450419  9.324398
##  FU2 data collected before COVID-19  9.677417 0.2310722 Inf  9.224524 10.130310
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.743717 0.2232954 Inf  9.306066 10.181368
##  FU2 data collected before COVID-19 10.111368 0.2315836 Inf  9.657472 10.565263
## 
## timefactor = 3, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.252856 0.2234918 Inf  9.814820 10.690892
##  FU2 data collected before COVID-19 10.579126 0.2313256 Inf 10.125736 11.032516
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.863417 0.2549423 Inf  9.363740 10.363095
##  FU2 data collected before COVID-19 10.064506 0.2407902 Inf  9.592566 10.536446
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.040998 0.2557090 Inf  9.539817 10.542178
##  FU2 data collected before COVID-19 10.282929 0.2415629 Inf  9.809474 10.756383
## 
## timefactor = 3, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.026294 0.2575882 Inf  9.521431 10.531158
##  FU2 data collected before COVID-19 10.488830 0.2413928 Inf 10.015709 10.961951
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_imm_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.6651072 0.1424855 Inf  -4.668  <.0001
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3148784 0.1436699 Inf  -2.192  0.0284
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1772359 0.1451827 Inf  -1.221  0.2222
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0870258 0.2007446 Inf  -0.434  0.6646
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2445549 0.2026868 Inf  -1.207  0.2276
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1825060 0.2051738 Inf  -0.890  0.3737
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.7900083 0.1440001 Inf  -5.486  <.0001
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3676509 0.1454072 Inf  -2.528  0.0115
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3262696 0.1453545 Inf  -2.245  0.0248
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2010890 0.1921443 Inf  -1.047  0.2953
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2419311 0.1941274 Inf  -1.246  0.2127
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.4625359 0.1962725 Inf  -2.357  0.0184
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_imm_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.6651072 0.1424855 Inf -0.9443736 -0.3858408
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.3148784 0.1436699 Inf -0.5964663 -0.0332906
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1772359 0.1451827 Inf -0.4617887  0.1073169
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.0870258 0.2007446 Inf -0.4804780  0.3064264
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.2445549 0.2026868 Inf -0.6418137  0.1527040
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1825060 0.2051738 Inf -0.5846393  0.2196273
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.7900083 0.1440001 Inf -1.0722433 -0.5077732
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.3676509 0.1454072 Inf -0.6526438 -0.0826581
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.3262696 0.1453545 Inf -0.6111591 -0.0413800
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.2010890 0.1921443 Inf -0.5776850  0.1755070
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.2419311 0.1941274 Inf -0.6224139  0.1385516
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.4625359 0.1962725 Inf -0.8472230 -0.0778488
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

11.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_11 <- summary(lsmeans(modelRVLT_imm_11, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTimmediate_lsmeans_11$Time<-NA
RVLTimmediate_lsmeans_11$Time[RVLTimmediate_lsmeans_11$timefactor==1]<-"Baseline"
RVLTimmediate_lsmeans_11$Time[RVLTimmediate_lsmeans_11$timefactor==2]<-"Follow-up 1"
RVLTimmediate_lsmeans_11$Time[RVLTimmediate_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "RVLT Immediate Score", title = "RVLT Immediate Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

11.2) RVLT Delayed Recall

11.2.1) Model

modelRVLT_del_11<- lmer(RVLT_Delayed_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= Tracking.data_long)
summary(modelRVLT_del_11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 130355.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8353 -0.5638 -0.0391  0.5149  4.9227 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.552    2.356   
##  Residual             7.672    2.770   
## Number of obs: 24749, groups:  ID, 8467
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                9.242e+00
## timefactor2                                                                8.826e-01
## timefactor3                                                                1.681e+00
## PandemicFU2 data collected before COVID-19                                 6.391e-01
## Age_sexFemales 65+                                                         1.421e+00
## Age_sexMales 45-64                                                        -5.731e-01
## Age_sexMales 65+                                                           6.437e-01
## EducationHigh School Diploma                                               4.089e-01
## EducationLess than High School Diploma                                     6.389e-01
## EducationSome College                                                      4.473e-01
## EthnicityWhite                                                             9.189e-01
## IncomeLevel>$150k                                                          7.148e-01
## IncomeLevel$100-150k                                                       6.172e-01
## IncomeLevel$20-50k                                                         1.905e-01
## IncomeLevel$50-100k                                                        5.736e-01
## BMI                                                                       -1.910e-02
## CESD.20.1                                                                 -3.615e-02
## SmokingStatusFormer Smoker                                                -2.425e-02
## SmokingStatusNever Smoked                                                  2.280e-01
## SmokingStatusOccasional Smoker                                             1.572e-01
## RelationshipstatusMarried                                                 -1.475e-02
## RelationshipstatusSeparated                                               -6.993e-02
## RelationshipstatusSingle                                                  -1.843e-01
## RelationshipstatusWidowed                                                 -8.461e-02
## LivingstatusAssisted Living                                               -1.052e+00
## LivingstatusHouse                                                          7.402e-02
## LivingstatusOther                                                         -8.581e-02
## AnxietyYes                                                                -1.013e-02
## MoodDisordYes                                                             -1.325e-01
## Chronicconditions                                                         -4.222e-02
## PASE_TOTALbaseline                                                         2.808e-03
## timefactor2:PandemicFU2 data collected before COVID-19                    -1.926e-01
## timefactor3:PandemicFU2 data collected before COVID-19                    -5.858e-01
## timefactor2:Age_sexFemales 65+                                            -9.016e-01
## timefactor3:Age_sexFemales 65+                                            -1.519e+00
## timefactor2:Age_sexMales 45-64                                            -1.369e-01
## timefactor3:Age_sexMales 45-64                                            -2.207e-01
## timefactor2:Age_sexMales 65+                                              -8.686e-01
## timefactor3:Age_sexMales 65+                                              -1.455e+00
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             -6.001e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.234e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               -6.464e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  5.637e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  8.513e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -9.969e-02
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -3.217e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    4.714e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    8.783e-01
##                                                                           Std. Error
## (Intercept)                                                                3.431e-01
## timefactor2                                                                1.194e-01
## timefactor3                                                                1.208e-01
## PandemicFU2 data collected before COVID-19                                 1.409e-01
## Age_sexFemales 65+                                                         1.987e-01
## Age_sexMales 45-64                                                         1.462e-01
## Age_sexMales 65+                                                           1.858e-01
## EducationHigh School Diploma                                               9.865e-02
## EducationLess than High School Diploma                                     1.395e-01
## EducationSome College                                                      1.226e-01
## EthnicityWhite                                                             1.960e-01
## IncomeLevel>$150k                                                          1.831e-01
## IncomeLevel$100-150k                                                       1.463e-01
## IncomeLevel$20-50k                                                         9.760e-02
## IncomeLevel$50-100k                                                        1.051e-01
## BMI                                                                        6.601e-03
## CESD.20.1                                                                  7.866e-03
## SmokingStatusFormer Smoker                                                 1.342e-01
## SmokingStatusNever Smoked                                                  1.398e-01
## SmokingStatusOccasional Smoker                                             2.649e-01
## RelationshipstatusMarried                                                  1.147e-01
## RelationshipstatusSeparated                                                2.256e-01
## RelationshipstatusSingle                                                   1.546e-01
## RelationshipstatusWidowed                                                  1.560e-01
## LivingstatusAssisted Living                                                4.644e-01
## LivingstatusHouse                                                          1.033e-01
## LivingstatusOther                                                          3.855e-01
## AnxietyYes                                                                 1.376e-01
## MoodDisordYes                                                              9.991e-02
## Chronicconditions                                                          1.599e-02
## PASE_TOTALbaseline                                                         4.551e-04
## timefactor2:PandemicFU2 data collected before COVID-19                     1.527e-01
## timefactor3:PandemicFU2 data collected before COVID-19                     1.538e-01
## timefactor2:Age_sexFemales 65+                                             2.072e-01
## timefactor3:Age_sexFemales 65+                                             2.110e-01
## timefactor2:Age_sexMales 45-64                                             1.559e-01
## timefactor3:Age_sexMales 45-64                                             1.570e-01
## timefactor2:Age_sexMales 65+                                               1.983e-01
## timefactor3:Age_sexMales 65+                                               2.012e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.432e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              2.000e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.363e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.636e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.669e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.172e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.180e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.577e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.596e-01
##                                                                                   df
## (Intercept)                                                                9.099e+03
## timefactor2                                                                1.636e+04
## timefactor3                                                                1.646e+04
## PandemicFU2 data collected before COVID-19                                 1.828e+04
## Age_sexFemales 65+                                                         1.735e+04
## Age_sexMales 45-64                                                         1.783e+04
## Age_sexMales 65+                                                           1.781e+04
## EducationHigh School Diploma                                               8.407e+03
## EducationLess than High School Diploma                                     8.507e+03
## EducationSome College                                                      8.365e+03
## EthnicityWhite                                                             8.443e+03
## IncomeLevel>$150k                                                          8.397e+03
## IncomeLevel$100-150k                                                       8.381e+03
## IncomeLevel$20-50k                                                         8.415e+03
## IncomeLevel$50-100k                                                        8.405e+03
## BMI                                                                        8.386e+03
## CESD.20.1                                                                  8.412e+03
## SmokingStatusFormer Smoker                                                 8.397e+03
## SmokingStatusNever Smoked                                                  8.397e+03
## SmokingStatusOccasional Smoker                                             8.373e+03
## RelationshipstatusMarried                                                  8.413e+03
## RelationshipstatusSeparated                                                8.455e+03
## RelationshipstatusSingle                                                   8.398e+03
## RelationshipstatusWidowed                                                  8.420e+03
## LivingstatusAssisted Living                                                8.446e+03
## LivingstatusHouse                                                          8.420e+03
## LivingstatusOther                                                          8.382e+03
## AnxietyYes                                                                 8.396e+03
## MoodDisordYes                                                              8.394e+03
## Chronicconditions                                                          8.401e+03
## PASE_TOTALbaseline                                                         8.400e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.635e+04
## timefactor3:PandemicFU2 data collected before COVID-19                     1.641e+04
## timefactor2:Age_sexFemales 65+                                             1.634e+04
## timefactor3:Age_sexFemales 65+                                             1.650e+04
## timefactor2:Age_sexMales 45-64                                             1.637e+04
## timefactor3:Age_sexMales 45-64                                             1.643e+04
## timefactor2:Age_sexMales 65+                                               1.638e+04
## timefactor3:Age_sexMales 65+                                               1.651e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.828e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.833e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.833e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.635e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.646e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.636e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.639e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.639e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.645e+04
##                                                                           t value
## (Intercept)                                                                26.939
## timefactor2                                                                 7.391
## timefactor3                                                                13.917
## PandemicFU2 data collected before COVID-19                                  4.536
## Age_sexFemales 65+                                                          7.151
## Age_sexMales 45-64                                                         -3.921
## Age_sexMales 65+                                                            3.465
## EducationHigh School Diploma                                                4.145
## EducationLess than High School Diploma                                      4.578
## EducationSome College                                                       3.648
## EthnicityWhite                                                              4.688
## IncomeLevel>$150k                                                           3.903
## IncomeLevel$100-150k                                                        4.219
## IncomeLevel$20-50k                                                          1.952
## IncomeLevel$50-100k                                                         5.458
## BMI                                                                        -2.893
## CESD.20.1                                                                  -4.595
## SmokingStatusFormer Smoker                                                 -0.181
## SmokingStatusNever Smoked                                                   1.631
## SmokingStatusOccasional Smoker                                              0.593
## RelationshipstatusMarried                                                  -0.129
## RelationshipstatusSeparated                                                -0.310
## RelationshipstatusSingle                                                   -1.192
## RelationshipstatusWidowed                                                  -0.542
## LivingstatusAssisted Living                                                -2.265
## LivingstatusHouse                                                           0.717
## LivingstatusOther                                                          -0.223
## AnxietyYes                                                                 -0.074
## MoodDisordYes                                                              -1.326
## Chronicconditions                                                          -2.641
## PASE_TOTALbaseline                                                          6.172
## timefactor2:PandemicFU2 data collected before COVID-19                     -1.262
## timefactor3:PandemicFU2 data collected before COVID-19                     -3.809
## timefactor2:Age_sexFemales 65+                                             -4.352
## timefactor3:Age_sexFemales 65+                                             -7.197
## timefactor2:Age_sexMales 45-64                                             -0.878
## timefactor3:Age_sexMales 45-64                                             -1.405
## timefactor2:Age_sexMales 65+                                               -4.380
## timefactor3:Age_sexMales 65+                                               -7.232
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              -2.468
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.617
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -2.735
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   2.138
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   3.189
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -0.459
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -0.148
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.830
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     3.383
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               1.53e-13
## timefactor3                                                                < 2e-16
## PandemicFU2 data collected before COVID-19                                5.77e-06
## Age_sexFemales 65+                                                        8.93e-13
## Age_sexMales 45-64                                                        8.86e-05
## Age_sexMales 65+                                                          0.000532
## EducationHigh School Diploma                                              3.43e-05
## EducationLess than High School Diploma                                    4.76e-06
## EducationSome College                                                     0.000266
## EthnicityWhite                                                            2.80e-06
## IncomeLevel>$150k                                                         9.56e-05
## IncomeLevel$100-150k                                                      2.47e-05
## IncomeLevel$20-50k                                                        0.050935
## IncomeLevel$50-100k                                                       4.94e-08
## BMI                                                                       0.003825
## CESD.20.1                                                                 4.38e-06
## SmokingStatusFormer Smoker                                                0.856672
## SmokingStatusNever Smoked                                                 0.102964
## SmokingStatusOccasional Smoker                                            0.552875
## RelationshipstatusMarried                                                 0.897699
## RelationshipstatusSeparated                                               0.756557
## RelationshipstatusSingle                                                  0.233345
## RelationshipstatusWidowed                                                 0.587536
## LivingstatusAssisted Living                                               0.023564
## LivingstatusHouse                                                         0.473589
## LivingstatusOther                                                         0.823844
## AnxietyYes                                                                0.941308
## MoodDisordYes                                                             0.184987
## Chronicconditions                                                         0.008283
## PASE_TOTALbaseline                                                        7.06e-10
## timefactor2:PandemicFU2 data collected before COVID-19                    0.206990
## timefactor3:PandemicFU2 data collected before COVID-19                    0.000140
## timefactor2:Age_sexFemales 65+                                            1.36e-05
## timefactor3:Age_sexFemales 65+                                            6.43e-13
## timefactor2:Age_sexMales 45-64                                            0.379697
## timefactor3:Age_sexMales 45-64                                            0.159992
## timefactor2:Age_sexMales 65+                                              1.19e-05
## timefactor3:Age_sexMales 65+                                              4.97e-13
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.013597
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.537085
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.006242
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.032505
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.001428
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.646282
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.882687
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.067313
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.000718
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## timefactor3                                                               ***
## PandemicFU2 data collected before COVID-19                                ***
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          ***
## EducationHigh School Diploma                                              ***
## EducationLess than High School Diploma                                    ***
## EducationSome College                                                     ***
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                         ***
## IncomeLevel$100-150k                                                      ***
## IncomeLevel$20-50k                                                        .  
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       ** 
## CESD.20.1                                                                 ***
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                               *  
## LivingstatusHouse                                                            
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         ** 
## PASE_TOTALbaseline                                                        ***
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor3:PandemicFU2 data collected before COVID-19                    ***
## timefactor2:Age_sexFemales 65+                                            ***
## timefactor3:Age_sexFemales 65+                                            ***
## timefactor2:Age_sexMales 45-64                                               
## timefactor3:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                              ***
## timefactor3:Age_sexMales 65+                                              ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             *  
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               ** 
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ *  
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ ** 
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   .  
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

11.2.2) Estimated marginal means

lsmeans(modelRVLT_del_11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.849428 0.2330947 Inf  9.392571 10.306286
##  FU2 data collected before COVID-19 10.488492 0.2243536 Inf 10.048767 10.928217
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.732000 0.2338147 Inf 10.273732 11.190269
##  FU2 data collected before COVID-19 11.178426 0.2246642 Inf 10.738092 11.618760
## 
## timefactor = 3, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.530816 0.2343965 Inf 11.071407 11.990225
##  FU2 data collected before COVID-19 11.584072 0.2246735 Inf 11.143720 12.024424
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.270153 0.2595151 Inf 10.761513 11.778793
##  FU2 data collected before COVID-19 11.309111 0.2432116 Inf 10.832425 11.785797
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.251169 0.2602680 Inf 10.741054 11.761285
##  FU2 data collected before COVID-19 11.661179 0.2442304 Inf 11.182497 12.139862
## 
## timefactor = 3, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.432781 0.2628893 Inf 10.917527 11.948034
##  FU2 data collected before COVID-19 11.737278 0.2443392 Inf 11.258382 12.216174
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.276376 0.2239543 Inf  8.837434  9.715319
##  FU2 data collected before COVID-19 10.038862 0.2318877 Inf  9.584370 10.493353
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.022024 0.2244885 Inf  9.582034 10.462013
##  FU2 data collected before COVID-19 10.492178 0.2323855 Inf 10.036710 10.947645
## 
## timefactor = 3, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.737100 0.2245403 Inf 10.297009 11.177191
##  FU2 data collected before COVID-19 10.881612 0.2323218 Inf 10.426270 11.336955
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.493155 0.2553214 Inf  9.992735 10.993576
##  FU2 data collected before COVID-19 10.485784 0.2414561 Inf 10.012539 10.959029
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.507100 0.2567392 Inf 10.003900 11.010299
##  FU2 data collected before COVID-19 10.778528 0.2427441 Inf 10.302758 11.254297
## 
## timefactor = 3, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.719691 0.2584393 Inf 10.213159 11.226223
##  FU2 data collected before COVID-19 11.004843 0.2423233 Inf 10.529898 11.479788
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_del_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.6390634 0.1408875 Inf  -4.536  <.0001
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.4464254 0.1423107 Inf  -3.137  0.0017
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0532563 0.1435597 Inf  -0.371  0.7107
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0389579 0.1985069 Inf  -0.196  0.8444
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.4100099 0.2007451 Inf  -2.042  0.0411
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3044979 0.2041795 Inf  -1.491  0.1359
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.7624855 0.1423852 Inf  -5.355  <.0001
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.4701538 0.1441308 Inf  -3.262  0.0011
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1445124 0.1440377 Inf  -1.003  0.3157
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   0.0073714 0.1899777 Inf   0.039  0.9690
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2714280 0.1934002 Inf  -1.403  0.1605
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2851517 0.1950666 Inf  -1.462  0.1438
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_del_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.6390634 0.1408875 Inf -0.9151979 -0.3629290
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.4464254 0.1423107 Inf -0.7253492 -0.1675016
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.0532563 0.1435597 Inf -0.3346282  0.2281156
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.0389579 0.1985069 Inf -0.4280242  0.3501084
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.4100099 0.2007451 Inf -0.8034630 -0.0165568
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.3044979 0.2041795 Inf -0.7046824  0.0956865
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.7624855 0.1423852 Inf -1.0415553 -0.4834157
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.4701538 0.1441308 Inf -0.7526448 -0.1876627
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1445124 0.1440377 Inf -0.4268212  0.1377963
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##   0.0073714 0.1899777 Inf -0.3649781  0.3797209
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.2714280 0.1934002 Inf -0.6504854  0.1076293
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.2851517 0.1950666 Inf -0.6674752  0.0971717
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

11.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_11 <- summary(lsmeans(modelRVLT_del_11, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTdelayed_lsmeans_11$Time<-NA
RVLTdelayed_lsmeans_11$Time[RVLTdelayed_lsmeans_11$timefactor==1]<-"Baseline"
RVLTdelayed_lsmeans_11$Time[RVLTdelayed_lsmeans_11$timefactor==2]<-"Follow-up 1"
RVLTdelayed_lsmeans_11$Time[RVLTdelayed_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "RVLT Delayed Score", title = "RVLT Delayed Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

11.3) Mental Alteration Test

11.3.1) Model

modelMAT_11<- lmer(MAT_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                    (1|ID), data= Tracking.data_long)
summary(modelMAT_11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## MAT_Normed ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +  
##     IncomeLevel + BMI + CESD.20.1 + SmokingStatus + Relationshipstatus +  
##     Livingstatus + Anxiety + MoodDisord + Chronicconditions +  
##     PASE_TOTALbaseline + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 123827.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0997 -0.4758  0.0038  0.4500  4.6476 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.090    2.256   
##  Residual             7.157    2.675   
## Number of obs: 23820, groups:  ID, 8467
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                9.443e+00
## timefactor2                                                                2.951e+00
## timefactor3                                                               -2.981e-01
## PandemicFU2 data collected before COVID-19                                 8.394e-02
## Age_sexFemales 65+                                                         2.050e-01
## Age_sexMales 45-64                                                        -4.177e-01
## Age_sexMales 65+                                                          -5.695e-01
## EducationHigh School Diploma                                              -6.593e-03
## EducationLess than High School Diploma                                     1.550e-02
## EducationSome College                                                      4.530e-02
## EthnicityWhite                                                             1.318e+00
## IncomeLevel>$150k                                                          9.046e-01
## IncomeLevel$100-150k                                                       9.814e-01
## IncomeLevel$20-50k                                                         3.624e-01
## IncomeLevel$50-100k                                                        7.254e-01
## BMI                                                                       -2.543e-02
## CESD.20.1                                                                 -4.506e-02
## SmokingStatusFormer Smoker                                                 2.002e-01
## SmokingStatusNever Smoked                                                  1.502e-01
## SmokingStatusOccasional Smoker                                             1.558e-01
## RelationshipstatusMarried                                                  1.714e-01
## RelationshipstatusSeparated                                               -1.933e-01
## RelationshipstatusSingle                                                   2.749e-01
## RelationshipstatusWidowed                                                 -1.197e-01
## LivingstatusAssisted Living                                               -7.241e-01
## LivingstatusHouse                                                         -1.815e-01
## LivingstatusOther                                                         -4.010e-01
## AnxietyYes                                                                 8.022e-02
## MoodDisordYes                                                              2.640e-01
## Chronicconditions                                                         -5.850e-02
## PASE_TOTALbaseline                                                        -1.046e-03
## timefactor2:PandemicFU2 data collected before COVID-19                    -1.545e+00
## timefactor3:PandemicFU2 data collected before COVID-19                     1.004e-01
## timefactor2:Age_sexFemales 65+                                            -7.842e-01
## timefactor3:Age_sexFemales 65+                                            -2.857e-01
## timefactor2:Age_sexMales 45-64                                            -3.091e+00
## timefactor3:Age_sexMales 45-64                                             2.006e-01
## timefactor2:Age_sexMales 65+                                              -3.046e+00
## timefactor3:Age_sexMales 65+                                              -1.617e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             -1.458e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              4.203e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                3.284e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.144e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  3.226e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.598e+00
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.611e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.616e+00
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.453e-02
##                                                                           Std. Error
## (Intercept)                                                                3.321e-01
## timefactor2                                                                1.170e-01
## timefactor3                                                                1.180e-01
## PandemicFU2 data collected before COVID-19                                 1.356e-01
## Age_sexFemales 65+                                                         1.913e-01
## Age_sexMales 45-64                                                         1.407e-01
## Age_sexMales 65+                                                           1.789e-01
## EducationHigh School Diploma                                               9.556e-02
## EducationLess than High School Diploma                                     1.357e-01
## EducationSome College                                                      1.185e-01
## EthnicityWhite                                                             1.900e-01
## IncomeLevel>$150k                                                          1.773e-01
## IncomeLevel$100-150k                                                       1.415e-01
## IncomeLevel$20-50k                                                         9.461e-02
## IncomeLevel$50-100k                                                        1.018e-01
## BMI                                                                        6.382e-03
## CESD.20.1                                                                  7.610e-03
## SmokingStatusFormer Smoker                                                 1.299e-01
## SmokingStatusNever Smoked                                                  1.352e-01
## SmokingStatusOccasional Smoker                                             2.563e-01
## RelationshipstatusMarried                                                  1.111e-01
## RelationshipstatusSeparated                                                2.181e-01
## RelationshipstatusSingle                                                   1.497e-01
## RelationshipstatusWidowed                                                  1.513e-01
## LivingstatusAssisted Living                                                4.499e-01
## LivingstatusHouse                                                          1.001e-01
## LivingstatusOther                                                          3.740e-01
## AnxietyYes                                                                 1.330e-01
## MoodDisordYes                                                              9.661e-02
## Chronicconditions                                                          1.550e-02
## PASE_TOTALbaseline                                                         4.401e-04
## timefactor2:PandemicFU2 data collected before COVID-19                     1.496e-01
## timefactor3:PandemicFU2 data collected before COVID-19                     1.502e-01
## timefactor2:Age_sexFemales 65+                                             2.067e-01
## timefactor3:Age_sexFemales 65+                                             2.087e-01
## timefactor2:Age_sexMales 45-64                                             1.530e-01
## timefactor3:Age_sexMales 45-64                                             1.536e-01
## timefactor2:Age_sexMales 65+                                               1.969e-01
## timefactor3:Age_sexMales 65+                                               2.008e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.340e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.924e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.275e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.616e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.636e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.134e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.133e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.551e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.581e-01
##                                                                                   df
## (Intercept)                                                                9.038e+03
## timefactor2                                                                1.557e+04
## timefactor3                                                                1.563e+04
## PandemicFU2 data collected before COVID-19                                 1.786e+04
## Age_sexFemales 65+                                                         1.697e+04
## Age_sexMales 45-64                                                         1.742e+04
## Age_sexMales 65+                                                           1.740e+04
## EducationHigh School Diploma                                               8.362e+03
## EducationLess than High School Diploma                                     8.554e+03
## EducationSome College                                                      8.271e+03
## EthnicityWhite                                                             8.432e+03
## IncomeLevel>$150k                                                          8.340e+03
## IncomeLevel$100-150k                                                       8.311e+03
## IncomeLevel$20-50k                                                         8.390e+03
## IncomeLevel$50-100k                                                        8.368e+03
## BMI                                                                        8.295e+03
## CESD.20.1                                                                  8.332e+03
## SmokingStatusFormer Smoker                                                 8.313e+03
## SmokingStatusNever Smoked                                                  8.312e+03
## SmokingStatusOccasional Smoker                                             8.293e+03
## RelationshipstatusMarried                                                  8.377e+03
## RelationshipstatusSeparated                                                8.367e+03
## RelationshipstatusSingle                                                   8.355e+03
## RelationshipstatusWidowed                                                  8.419e+03
## LivingstatusAssisted Living                                                8.385e+03
## LivingstatusHouse                                                          8.398e+03
## LivingstatusOther                                                          8.384e+03
## AnxietyYes                                                                 8.312e+03
## MoodDisordYes                                                              8.304e+03
## Chronicconditions                                                          8.384e+03
## PASE_TOTALbaseline                                                         8.314e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.555e+04
## timefactor3:PandemicFU2 data collected before COVID-19                     1.558e+04
## timefactor2:Age_sexFemales 65+                                             1.573e+04
## timefactor3:Age_sexFemales 65+                                             1.580e+04
## timefactor2:Age_sexMales 45-64                                             1.559e+04
## timefactor3:Age_sexMales 45-64                                             1.562e+04
## timefactor2:Age_sexMales 65+                                               1.576e+04
## timefactor3:Age_sexMales 65+                                               1.591e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.786e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.790e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.791e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.568e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.574e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.558e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.557e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.572e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.581e+04
##                                                                           t value
## (Intercept)                                                                28.433
## timefactor2                                                                25.212
## timefactor3                                                                -2.527
## PandemicFU2 data collected before COVID-19                                  0.619
## Age_sexFemales 65+                                                          1.071
## Age_sexMales 45-64                                                         -2.969
## Age_sexMales 65+                                                           -3.184
## EducationHigh School Diploma                                               -0.069
## EducationLess than High School Diploma                                      0.114
## EducationSome College                                                       0.382
## EthnicityWhite                                                              6.933
## IncomeLevel>$150k                                                           5.103
## IncomeLevel$100-150k                                                        6.935
## IncomeLevel$20-50k                                                          3.830
## IncomeLevel$50-100k                                                         7.125
## BMI                                                                        -3.985
## CESD.20.1                                                                  -5.921
## SmokingStatusFormer Smoker                                                  1.542
## SmokingStatusNever Smoked                                                   1.111
## SmokingStatusOccasional Smoker                                              0.608
## RelationshipstatusMarried                                                   1.542
## RelationshipstatusSeparated                                                -0.886
## RelationshipstatusSingle                                                    1.836
## RelationshipstatusWidowed                                                  -0.791
## LivingstatusAssisted Living                                                -1.610
## LivingstatusHouse                                                          -1.813
## LivingstatusOther                                                          -1.072
## AnxietyYes                                                                  0.603
## MoodDisordYes                                                               2.733
## Chronicconditions                                                          -3.774
## PASE_TOTALbaseline                                                         -2.377
## timefactor2:PandemicFU2 data collected before COVID-19                    -10.332
## timefactor3:PandemicFU2 data collected before COVID-19                      0.668
## timefactor2:Age_sexFemales 65+                                             -3.793
## timefactor3:Age_sexFemales 65+                                             -1.369
## timefactor2:Age_sexMales 45-64                                            -20.201
## timefactor3:Age_sexMales 45-64                                              1.306
## timefactor2:Age_sexMales 65+                                              -15.468
## timefactor3:Age_sexMales 65+                                               -0.805
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              -0.623
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.218
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 1.444
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.437
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   1.224
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   7.487
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -0.755
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     6.332
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     0.095
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                                < 2e-16
## timefactor3                                                               0.011520
## PandemicFU2 data collected before COVID-19                                0.535900
## Age_sexFemales 65+                                                        0.283960
## Age_sexMales 45-64                                                        0.002994
## Age_sexMales 65+                                                          0.001455
## EducationHigh School Diploma                                              0.944999
## EducationLess than High School Diploma                                    0.909086
## EducationSome College                                                     0.702304
## EthnicityWhite                                                            4.44e-12
## IncomeLevel>$150k                                                         3.42e-07
## IncomeLevel$100-150k                                                      4.37e-12
## IncomeLevel$20-50k                                                        0.000129
## IncomeLevel$50-100k                                                       1.13e-12
## BMI                                                                       6.82e-05
## CESD.20.1                                                                 3.33e-09
## SmokingStatusFormer Smoker                                                0.123099
## SmokingStatusNever Smoked                                                 0.266557
## SmokingStatusOccasional Smoker                                            0.543454
## RelationshipstatusMarried                                                 0.123066
## RelationshipstatusSeparated                                               0.375527
## RelationshipstatusSingle                                                  0.066416
## RelationshipstatusWidowed                                                 0.428945
## LivingstatusAssisted Living                                               0.107522
## LivingstatusHouse                                                         0.069933
## LivingstatusOther                                                         0.283704
## AnxietyYes                                                                0.546504
## MoodDisordYes                                                             0.006290
## Chronicconditions                                                         0.000162
## PASE_TOTALbaseline                                                        0.017477
## timefactor2:PandemicFU2 data collected before COVID-19                     < 2e-16
## timefactor3:PandemicFU2 data collected before COVID-19                    0.503906
## timefactor2:Age_sexFemales 65+                                            0.000149
## timefactor3:Age_sexFemales 65+                                            0.171048
## timefactor2:Age_sexMales 45-64                                             < 2e-16
## timefactor3:Age_sexMales 45-64                                            0.191435
## timefactor2:Age_sexMales 65+                                               < 2e-16
## timefactor3:Age_sexMales 65+                                              0.420633
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.533193
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.827117
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.148849
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.661852
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.221045
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 7.40e-14
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.450039
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   2.48e-10
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.924286
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## timefactor3                                                               *  
## PandemicFU2 data collected before COVID-19                                   
## Age_sexFemales 65+                                                           
## Age_sexMales 45-64                                                        ** 
## Age_sexMales 65+                                                          ** 
## EducationHigh School Diploma                                                 
## EducationLess than High School Diploma                                       
## EducationSome College                                                        
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                         ***
## IncomeLevel$100-150k                                                      ***
## IncomeLevel$20-50k                                                        ***
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       ***
## CESD.20.1                                                                 ***
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                  .  
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         .  
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                             ** 
## Chronicconditions                                                         ***
## PASE_TOTALbaseline                                                        *  
## timefactor2:PandemicFU2 data collected before COVID-19                    ***
## timefactor3:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                            ***
## timefactor3:Age_sexFemales 65+                                               
## timefactor2:Age_sexMales 45-64                                            ***
## timefactor3:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                              ***
## timefactor3:Age_sexMales 65+                                                 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 ***
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   ***
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

11.3.2) Estimated marginal means

Significant differences for males 65+

lsmeans(modelMAT_11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.463475 0.2255691 Inf  9.021367  9.905582
##  FU2 data collected before COVID-19  9.547411 0.2172108 Inf  9.121686  9.973136
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  12.414464 0.2271972 Inf 11.969165 12.859762
##  FU2 data collected before COVID-19 10.952979 0.2181102 Inf 10.525491 11.380467
## 
## timefactor = 3, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.165364 0.2274768 Inf  8.719518  9.611210
##  FU2 data collected before COVID-19  9.349674 0.2179146 Inf  8.922569  9.776779
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.668448 0.2509308 Inf  9.176633 10.160263
##  FU2 data collected before COVID-19  9.606555 0.2353552 Inf  9.145267 10.067842
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.835241 0.2562129 Inf 11.333073 12.337409
##  FU2 data collected before COVID-19 10.342360 0.2376782 Inf  9.876519 10.808201
## 
## timefactor = 3, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.084621 0.2569091 Inf  8.581088  9.588154
##  FU2 data collected before COVID-19  9.445706 0.2380809 Inf  8.979076  9.912336
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.045802 0.2167912 Inf  8.620899  9.470705
##  FU2 data collected before COVID-19  9.171767 0.2244012 Inf  8.731949  9.611586
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   8.905959 0.2181591 Inf  8.478375  9.333543
##  FU2 data collected before COVID-19  9.084069 0.2261540 Inf  8.640815  9.527323
## 
## timefactor = 3, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   8.948295 0.2179078 Inf  8.521204  9.375387
##  FU2 data collected before COVID-19  9.013503 0.2257825 Inf  8.570977  9.456029
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   8.894001 0.2469340 Inf  8.410019  9.377983
##  FU2 data collected before COVID-19  9.306310 0.2336362 Inf  8.848392  9.764229
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   8.798722 0.2519857 Inf  8.304839  9.292605
##  FU2 data collected before COVID-19  9.281193 0.2366219 Inf  8.817423  9.744964
## 
## timefactor = 3, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   8.434179 0.2543076 Inf  7.935745  8.932612
##  FU2 data collected before COVID-19  8.971393 0.2367166 Inf  8.507437  9.435349
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelMAT_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0839362 0.1355916 Inf  -0.619  0.5359
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   1.4614851 0.1392757 Inf  10.493  <.0001
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1843099 0.1399270 Inf  -1.317  0.1878
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   0.0618933 0.1910534 Inf   0.324  0.7460
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   1.4928811 0.2008567 Inf   7.433  <.0001
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3610846 0.2029033 Inf  -1.780  0.0751
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1259655 0.1370328 Inf  -0.919  0.3580
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1781102 0.1418371 Inf  -1.256  0.2092
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0652074 0.1411178 Inf  -0.462  0.6440
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.4123094 0.1828342 Inf  -2.255  0.0241
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.4824717 0.1928114 Inf  -2.502  0.0123
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.5372147 0.1962695 Inf  -2.737  0.0062
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelMAT_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.0839362 0.1355916 Inf -0.3496908  0.1818183
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##   1.4614851 0.1392757 Inf  1.1885098  1.7344604
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1843099 0.1399270 Inf -0.4585617  0.0899419
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##   0.0618933 0.1910534 Inf -0.3125645  0.4363512
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##   1.4928811 0.2008567 Inf  1.0992091  1.8865530
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.3610846 0.2029033 Inf -0.7587678  0.0365986
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1259655 0.1370328 Inf -0.3945449  0.1426138
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1781102 0.1418371 Inf -0.4561058  0.0998854
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.0652074 0.1411178 Inf -0.3417932  0.2113783
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.4123094 0.1828342 Inf -0.7706579 -0.0539610
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.4824717 0.1928114 Inf -0.8603751 -0.1045683
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.5372147 0.1962695 Inf -0.9218958 -0.1525337
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

11.3.3) Graph of estimated marginal means

MAT_lsmeans_11 <- summary(lsmeans(modelMAT_11, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.

MAT_lsmeans_11$Time<-NA
MAT_lsmeans_11$Time[MAT_lsmeans_11$timefactor==1]<-"Baseline"
MAT_lsmeans_11$Time[MAT_lsmeans_11$timefactor==2]<-"Follow-up 1"
MAT_lsmeans_11$Time[MAT_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(MAT_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "MAT Score", title = "Mental Alteration Test Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

11.4) Animal Fluency

11.4.1) Model

modelAnimals_11<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= Tracking.data_long)
summary(modelAnimals_11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Animal_Fluency_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 116884.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.3041 -0.5635 -0.0137  0.5488  4.7736 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.916    2.217   
##  Residual             3.618    1.902   
## Number of obs: 25010, groups:  ID, 8467
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                8.850e+00
## timefactor2                                                               -5.901e-02
## timefactor3                                                                2.526e-01
## PandemicFU2 data collected before COVID-19                                 2.614e-01
## Age_sexFemales 65+                                                         2.563e-01
## Age_sexMales 45-64                                                        -8.441e-02
## Age_sexMales 65+                                                          -1.303e-01
## EducationHigh School Diploma                                               3.040e-01
## EducationLess than High School Diploma                                     4.347e-01
## EducationSome College                                                      2.537e-01
## EthnicityWhite                                                             1.314e+00
## IncomeLevel>$150k                                                          6.146e-01
## IncomeLevel$100-150k                                                       5.214e-01
## IncomeLevel$20-50k                                                         8.887e-02
## IncomeLevel$50-100k                                                        3.397e-01
## BMI                                                                       -1.682e-02
## CESD.20.1                                                                 -3.603e-02
## SmokingStatusFormer Smoker                                                 2.208e-01
## SmokingStatusNever Smoked                                                  2.246e-01
## SmokingStatusOccasional Smoker                                             2.834e-01
## RelationshipstatusMarried                                                 -1.833e-01
## RelationshipstatusSeparated                                               -1.667e-01
## RelationshipstatusSingle                                                  -9.008e-02
## RelationshipstatusWidowed                                                 -1.894e-01
## LivingstatusAssisted Living                                               -1.139e-01
## LivingstatusHouse                                                          3.327e-01
## LivingstatusOther                                                          1.223e-01
## AnxietyYes                                                                -7.006e-02
## MoodDisordYes                                                              1.948e-01
## Chronicconditions                                                         -1.098e-02
## PASE_TOTALbaseline                                                         7.644e-04
## timefactor2:PandemicFU2 data collected before COVID-19                     1.302e-01
## timefactor3:PandemicFU2 data collected before COVID-19                     2.521e-02
## timefactor2:Age_sexFemales 65+                                            -3.627e-01
## timefactor3:Age_sexFemales 65+                                            -7.586e-01
## timefactor2:Age_sexMales 45-64                                            -2.931e-02
## timefactor3:Age_sexMales 45-64                                            -2.189e-01
## timefactor2:Age_sexMales 65+                                              -1.239e-01
## timefactor3:Age_sexMales 65+                                              -6.604e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             -9.044e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             -1.666e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               -1.117e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  6.846e-02
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.871e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.727e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.783e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   -1.620e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.080e-01
##                                                                           Std. Error
## (Intercept)                                                                2.948e-01
## timefactor2                                                                8.179e-02
## timefactor3                                                                8.281e-02
## PandemicFU2 data collected before COVID-19                                 1.132e-01
## Age_sexFemales 65+                                                         1.606e-01
## Age_sexMales 45-64                                                         1.178e-01
## Age_sexMales 65+                                                           1.498e-01
## EducationHigh School Diploma                                               8.544e-02
## EducationLess than High School Diploma                                     1.205e-01
## EducationSome College                                                      1.063e-01
## EthnicityWhite                                                             1.693e-01
## IncomeLevel>$150k                                                          1.586e-01
## IncomeLevel$100-150k                                                       1.267e-01
## IncomeLevel$20-50k                                                         8.449e-02
## IncomeLevel$50-100k                                                        9.100e-02
## BMI                                                                        5.717e-03
## CESD.20.1                                                                  6.810e-03
## SmokingStatusFormer Smoker                                                 1.163e-01
## SmokingStatusNever Smoked                                                  1.211e-01
## SmokingStatusOccasional Smoker                                             2.295e-01
## RelationshipstatusMarried                                                  9.935e-02
## RelationshipstatusSeparated                                                1.951e-01
## RelationshipstatusSingle                                                   1.339e-01
## RelationshipstatusWidowed                                                  1.350e-01
## LivingstatusAssisted Living                                                4.016e-01
## LivingstatusHouse                                                          8.942e-02
## LivingstatusOther                                                          3.334e-01
## AnxietyYes                                                                 1.191e-01
## MoodDisordYes                                                              8.655e-02
## Chronicconditions                                                          1.384e-02
## PASE_TOTALbaseline                                                         3.940e-04
## timefactor2:PandemicFU2 data collected before COVID-19                     1.046e-01
## timefactor3:PandemicFU2 data collected before COVID-19                     1.054e-01
## timefactor2:Age_sexFemales 65+                                             1.416e-01
## timefactor3:Age_sexFemales 65+                                             1.438e-01
## timefactor2:Age_sexMales 45-64                                             1.067e-01
## timefactor3:Age_sexMales 45-64                                             1.077e-01
## timefactor2:Age_sexMales 65+                                               1.351e-01
## timefactor3:Age_sexMales 65+                                               1.373e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.954e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.607e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.899e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.801e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.820e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.487e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.493e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.756e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.773e-01
##                                                                                   df
## (Intercept)                                                                8.860e+03
## timefactor2                                                                1.658e+04
## timefactor3                                                                1.665e+04
## PandemicFU2 data collected before COVID-19                                 1.504e+04
## Age_sexFemales 65+                                                         1.432e+04
## Age_sexMales 45-64                                                         1.468e+04
## Age_sexMales 65+                                                           1.467e+04
## EducationHigh School Diploma                                               8.443e+03
## EducationLess than High School Diploma                                     8.467e+03
## EducationSome College                                                      8.417e+03
## EthnicityWhite                                                             8.414e+03
## IncomeLevel>$150k                                                          8.433e+03
## IncomeLevel$100-150k                                                       8.424e+03
## IncomeLevel$20-50k                                                         8.435e+03
## IncomeLevel$50-100k                                                        8.435e+03
## BMI                                                                        8.424e+03
## CESD.20.1                                                                  8.436e+03
## SmokingStatusFormer Smoker                                                 8.435e+03
## SmokingStatusNever Smoked                                                  8.434e+03
## SmokingStatusOccasional Smoker                                             8.422e+03
## RelationshipstatusMarried                                                  8.450e+03
## RelationshipstatusSeparated                                                8.457e+03
## RelationshipstatusSingle                                                   8.442e+03
## RelationshipstatusWidowed                                                  8.437e+03
## LivingstatusAssisted Living                                                8.437e+03
## LivingstatusHouse                                                          8.446e+03
## LivingstatusOther                                                          8.373e+03
## AnxietyYes                                                                 8.432e+03
## MoodDisordYes                                                              8.436e+03
## Chronicconditions                                                          8.425e+03
## PASE_TOTALbaseline                                                         8.426e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.657e+04
## timefactor3:PandemicFU2 data collected before COVID-19                     1.662e+04
## timefactor2:Age_sexFemales 65+                                             1.656e+04
## timefactor3:Age_sexFemales 65+                                             1.665e+04
## timefactor2:Age_sexMales 45-64                                             1.658e+04
## timefactor3:Age_sexMales 45-64                                             1.664e+04
## timefactor2:Age_sexMales 65+                                               1.656e+04
## timefactor3:Age_sexMales 65+                                               1.666e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.504e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.507e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.507e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.656e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.662e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.657e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.660e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.657e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.662e+04
##                                                                           t value
## (Intercept)                                                                30.019
## timefactor2                                                                -0.722
## timefactor3                                                                 3.050
## PandemicFU2 data collected before COVID-19                                  2.309
## Age_sexFemales 65+                                                          1.596
## Age_sexMales 45-64                                                         -0.716
## Age_sexMales 65+                                                           -0.870
## EducationHigh School Diploma                                                3.558
## EducationLess than High School Diploma                                      3.606
## EducationSome College                                                       2.388
## EthnicityWhite                                                              7.762
## IncomeLevel>$150k                                                           3.875
## IncomeLevel$100-150k                                                        4.115
## IncomeLevel$20-50k                                                          1.052
## IncomeLevel$50-100k                                                         3.733
## BMI                                                                        -2.942
## CESD.20.1                                                                  -5.291
## SmokingStatusFormer Smoker                                                  1.899
## SmokingStatusNever Smoked                                                   1.855
## SmokingStatusOccasional Smoker                                              1.235
## RelationshipstatusMarried                                                  -1.845
## RelationshipstatusSeparated                                                -0.854
## RelationshipstatusSingle                                                   -0.673
## RelationshipstatusWidowed                                                  -1.403
## LivingstatusAssisted Living                                                -0.284
## LivingstatusHouse                                                           3.721
## LivingstatusOther                                                           0.367
## AnxietyYes                                                                 -0.588
## MoodDisordYes                                                               2.251
## Chronicconditions                                                          -0.793
## PASE_TOTALbaseline                                                          1.940
## timefactor2:PandemicFU2 data collected before COVID-19                      1.245
## timefactor3:PandemicFU2 data collected before COVID-19                      0.239
## timefactor2:Age_sexFemales 65+                                             -2.561
## timefactor3:Age_sexFemales 65+                                             -5.274
## timefactor2:Age_sexMales 45-64                                             -0.275
## timefactor3:Age_sexMales 45-64                                             -2.032
## timefactor2:Age_sexMales 65+                                               -0.917
## timefactor3:Age_sexMales 65+                                               -4.808
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              -0.463
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              -1.037
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -0.588
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.380
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   1.028
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -1.161
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -0.119
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    -0.923
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.173
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               0.470590
## timefactor3                                                               0.002289
## PandemicFU2 data collected before COVID-19                                0.020977
## Age_sexFemales 65+                                                        0.110605
## Age_sexMales 45-64                                                        0.473713
## Age_sexMales 65+                                                          0.384481
## EducationHigh School Diploma                                              0.000376
## EducationLess than High School Diploma                                    0.000313
## EducationSome College                                                     0.016984
## EthnicityWhite                                                            9.34e-15
## IncomeLevel>$150k                                                         0.000107
## IncomeLevel$100-150k                                                      3.91e-05
## IncomeLevel$20-50k                                                        0.292906
## IncomeLevel$50-100k                                                       0.000190
## BMI                                                                       0.003265
## CESD.20.1                                                                 1.25e-07
## SmokingStatusFormer Smoker                                                0.057580
## SmokingStatusNever Smoked                                                 0.063618
## SmokingStatusOccasional Smoker                                            0.216977
## RelationshipstatusMarried                                                 0.065078
## RelationshipstatusSeparated                                               0.392982
## RelationshipstatusSingle                                                  0.501279
## RelationshipstatusWidowed                                                 0.160664
## LivingstatusAssisted Living                                               0.776684
## LivingstatusHouse                                                         0.000200
## LivingstatusOther                                                         0.713840
## AnxietyYes                                                                0.556517
## MoodDisordYes                                                             0.024421
## Chronicconditions                                                         0.427715
## PASE_TOTALbaseline                                                        0.052405
## timefactor2:PandemicFU2 data collected before COVID-19                    0.213050
## timefactor3:PandemicFU2 data collected before COVID-19                    0.810898
## timefactor2:Age_sexFemales 65+                                            0.010442
## timefactor3:Age_sexFemales 65+                                            1.35e-07
## timefactor2:Age_sexMales 45-64                                            0.783450
## timefactor3:Age_sexMales 45-64                                            0.042128
## timefactor2:Age_sexMales 65+                                              0.359398
## timefactor3:Age_sexMales 65+                                              1.54e-06
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.643540
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.299825
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.556239
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.703939
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.303965
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.245481
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.904935
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.356151
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.240625
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                                  
## timefactor3                                                               ** 
## PandemicFU2 data collected before COVID-19                                *  
## Age_sexFemales 65+                                                           
## Age_sexMales 45-64                                                           
## Age_sexMales 65+                                                             
## EducationHigh School Diploma                                              ***
## EducationLess than High School Diploma                                    ***
## EducationSome College                                                     *  
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                         ***
## IncomeLevel$100-150k                                                      ***
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       ** 
## CESD.20.1                                                                 ***
## SmokingStatusFormer Smoker                                                .  
## SmokingStatusNever Smoked                                                 .  
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                 .  
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         ***
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                             *  
## Chronicconditions                                                            
## PASE_TOTALbaseline                                                        .  
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor3:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                            *  
## timefactor3:Age_sexFemales 65+                                            ***
## timefactor2:Age_sexMales 45-64                                               
## timefactor3:Age_sexMales 45-64                                            *  
## timefactor2:Age_sexMales 65+                                                 
## timefactor3:Age_sexMales 65+                                              ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

11.4.2) Estimated marginal means

Significant differences for females 45-64 and 65+ and significant differences males 65+

lsmeans(modelAnimals_11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.740634 0.1985793 Inf  9.351426 10.129843
##  FU2 data collected before COVID-19 10.002053 0.1920666 Inf  9.625610 10.378497
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.681621 0.1988456 Inf  9.291891 10.071351
##  FU2 data collected before COVID-19 10.073236 0.1921793 Inf  9.696571 10.449900
## 
## timefactor = 3, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.993245 0.1992267 Inf  9.602768 10.383722
##  FU2 data collected before COVID-19 10.279876 0.1921899 Inf  9.903191 10.656561
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.996888 0.2188953 Inf  9.567861 10.425915
##  FU2 data collected before COVID-19 10.167868 0.2068014 Inf  9.762545 10.573191
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.575133 0.2190351 Inf  9.145832 10.004434
##  FU2 data collected before COVID-19  9.944764 0.2069864 Inf  9.539078 10.350450
## 
## timefactor = 3, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.490882 0.2200025 Inf  9.059685  9.922079
##  FU2 data collected before COVID-19  9.874167 0.2071267 Inf  9.468206 10.280128
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.656228 0.1915284 Inf  9.280839 10.031616
##  FU2 data collected before COVID-19  9.751059 0.1976118 Inf  9.363747 10.138372
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.567904 0.1917031 Inf  9.192173  9.943635
##  FU2 data collected before COVID-19  9.620233 0.1977969 Inf  9.232558 10.007908
## 
## timefactor = 3, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.689977 0.1918400 Inf  9.313978 10.065977
##  FU2 data collected before COVID-19  9.792190 0.1976952 Inf  9.404715 10.179666
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.610374 0.2159710 Inf  9.187078 10.033669
##  FU2 data collected before COVID-19  9.760046 0.2051867 Inf  9.357887 10.162204
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.427494 0.2161783 Inf  9.003792  9.851195
##  FU2 data collected before COVID-19  9.545366 0.2053791 Inf  9.142830  9.947901
## 
## timefactor = 3, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.202597 0.2172110 Inf  8.776871  9.628322
##  FU2 data collected before COVID-19  9.585514 0.2054154 Inf  9.182907  9.988121
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelAnimals_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2614191 0.1132349 Inf  -2.309  0.0210
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3916145 0.1138134 Inf  -3.441  0.0006
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2866313 0.1145714 Inf  -2.502  0.0124
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1709802 0.1595874 Inf  -1.071  0.2840
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3696308 0.1600727 Inf  -2.309  0.0209
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3832849 0.1616409 Inf  -2.371  0.0177
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0948318 0.1144394 Inf  -0.829  0.4073
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0523290 0.1150952 Inf  -0.455  0.6494
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1022127 0.1151366 Inf  -0.888  0.3747
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1496718 0.1526477 Inf  -0.981  0.3268
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1178723 0.1532604 Inf  -0.769  0.4418
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3829171 0.1546809 Inf  -2.476  0.0133
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelAnimals_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.2614191 0.1132349 Inf -0.4833555 -0.03948266
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.3916145 0.1138134 Inf -0.6146846 -0.16854440
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.2866313 0.1145714 Inf -0.5111871 -0.06207549
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.1709802 0.1595874 Inf -0.4837658  0.14180531
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.3696308 0.1600727 Inf -0.6833674 -0.05589413
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.3832849 0.1616409 Inf -0.7000952 -0.06647459
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.0948318 0.1144394 Inf -0.3191289  0.12946523
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.0523290 0.1150952 Inf -0.2779115  0.17325347
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.1022127 0.1151366 Inf -0.3278762  0.12345080
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.1496718 0.1526477 Inf -0.4488558  0.14951221
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.1178723 0.1532604 Inf -0.4182572  0.18251268
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.3829171 0.1546809 Inf -0.6860861 -0.07974808
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

11.4.3) Graph of estimated marginal means

Animals_lsmeans_11 <- summary(lsmeans(modelAnimals_11, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.

Animals_lsmeans_11$Time<-NA
Animals_lsmeans_11$Time[Animals_lsmeans_11$timefactor==1]<-"Baseline"
Animals_lsmeans_11$Time[Animals_lsmeans_11$timefactor==2]<-"Follow-up 1"
Animals_lsmeans_11$Time[Animals_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(Animals_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "Animal Fluency (words)", title = "Animal Fluency Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

12) Age and sex differences in time x pandemic effects on cognitive functon ( adjusting for baseline)

All models use normalized cognitive scores. Each model is adjusted for education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, baseline PASE score, and baseline cognitive performance

Age and Sex grouping

Tracking.data_long_2$Age_sex<-NA
Tracking.data_long_2$Age_sex[Tracking.data_long_2$Age<=64 & Tracking.data_long_2$Sex == "M"]<-"Males 45-64"
Tracking.data_long_2$Age_sex[Tracking.data_long_2$Age<=64 & Tracking.data_long_2$Sex == "F"]<-"Females 45-64"
Tracking.data_long_2$Age_sex[Tracking.data_long_2$Age>64 & Tracking.data_long_2$Sex == "M"]<-"Males 65+"
Tracking.data_long_2$Age_sex[Tracking.data_long_2$Age>64 & Tracking.data_long_2$Sex == "F"]<-"Females 65+"

12.1) RVLT Immediate Recall

12.1.1) Model

modelRVLT_imm_adj11<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +
                            (1|ID), data= Tracking.data_long_2)
summary(modelRVLT_imm_adj11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Immediate_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +  
##     (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 85600.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6937 -0.5662 -0.0379  0.5265  3.8799 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.106    2.026   
##  Residual             7.332    2.708   
## Number of obs: 16420, groups:  ID, 8448
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                6.598e+00
## timefactor2                                                                4.080e-01
## PandemicFU2 data collected before COVID-19                                 1.074e-01
## Age_sexFemales 65+                                                        -8.511e-02
## Age_sexMales 45-64                                                        -7.229e-01
## Age_sexMales 65+                                                          -7.033e-01
## EducationHigh School Diploma                                               2.655e-01
## EducationLess than High School Diploma                                     4.949e-01
## EducationSome College                                                      2.222e-01
## EthnicityWhite                                                             4.706e-01
## IncomeLevel>$150k                                                          4.902e-01
## IncomeLevel$100-150k                                                       4.279e-01
## IncomeLevel$20-50k                                                         1.359e-01
## IncomeLevel$50-100k                                                        4.270e-01
## BMI                                                                       -1.495e-02
## CESD.10baseline                                                           -2.478e-02
## SmokingStatusFormer Smoker                                                 1.541e-01
## SmokingStatusNever Smoked                                                  2.666e-01
## SmokingStatusOccasional Smoker                                            -2.947e-02
## RelationshipstatusMarried                                                  3.205e-01
## RelationshipstatusSeparated                                                2.465e-01
## RelationshipstatusSingle                                                   2.052e-01
## RelationshipstatusWidowed                                                 -2.142e-02
## LivingstatusAssisted Living                                               -8.691e-01
## LivingstatusHouse                                                          4.813e-02
## LivingstatusOther                                                         -3.586e-01
## AnxietyYes                                                                 8.277e-02
## MoodDisordYes                                                             -1.564e-01
## Chronicconditions                                                         -2.961e-02
## PASE_TOTALbaseline                                                         2.632e-03
## RVLT_Immediate_Normedbaseline                                              3.501e-01
## timefactor2:PandemicFU2 data collected before COVID-19                    -1.361e-01
## timefactor2:Age_sexFemales 65+                                            -4.960e-01
## timefactor2:Age_sexMales 45-64                                             9.980e-02
## timefactor2:Age_sexMales 65+                                              -4.202e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.003e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             -4.939e-03
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                8.141e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  7.797e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  9.555e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    3.542e-01
##                                                                           Std. Error
## (Intercept)                                                                3.435e-01
## timefactor2                                                                1.191e-01
## PandemicFU2 data collected before COVID-19                                 1.324e-01
## Age_sexFemales 65+                                                         1.874e-01
## Age_sexMales 45-64                                                         1.378e-01
## Age_sexMales 65+                                                           1.749e-01
## EducationHigh School Diploma                                               9.709e-02
## EducationLess than High School Diploma                                     1.376e-01
## EducationSome College                                                      1.206e-01
## EthnicityWhite                                                             1.923e-01
## IncomeLevel>$150k                                                          1.805e-01
## IncomeLevel$100-150k                                                       1.438e-01
## IncomeLevel$20-50k                                                         9.605e-02
## IncomeLevel$50-100k                                                        1.035e-01
## BMI                                                                        6.494e-03
## CESD.10baseline                                                            7.739e-03
## SmokingStatusFormer Smoker                                                 1.321e-01
## SmokingStatusNever Smoked                                                  1.376e-01
## SmokingStatusOccasional Smoker                                             2.601e-01
## RelationshipstatusMarried                                                  1.129e-01
## RelationshipstatusSeparated                                                2.222e-01
## RelationshipstatusSingle                                                   1.522e-01
## RelationshipstatusWidowed                                                  1.536e-01
## LivingstatusAssisted Living                                                4.571e-01
## LivingstatusHouse                                                          1.016e-01
## LivingstatusOther                                                          3.779e-01
## AnxietyYes                                                                 1.353e-01
## MoodDisordYes                                                              9.832e-02
## Chronicconditions                                                          1.572e-02
## PASE_TOTALbaseline                                                         4.477e-04
## RVLT_Immediate_Normedbaseline                                              8.154e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.514e-01
## timefactor2:Age_sexFemales 65+                                             2.067e-01
## timefactor2:Age_sexMales 45-64                                             1.547e-01
## timefactor2:Age_sexMales 65+                                               1.976e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.286e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.879e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.222e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.619e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.145e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.551e-01
##                                                                                   df
## (Intercept)                                                                8.822e+03
## timefactor2                                                                8.318e+03
## PandemicFU2 data collected before COVID-19                                 1.461e+04
## Age_sexFemales 65+                                                         1.414e+04
## Age_sexMales 45-64                                                         1.440e+04
## Age_sexMales 65+                                                           1.439e+04
## EducationHigh School Diploma                                               8.371e+03
## EducationLess than High School Diploma                                     8.502e+03
## EducationSome College                                                      8.293e+03
## EthnicityWhite                                                             8.288e+03
## IncomeLevel>$150k                                                          8.368e+03
## IncomeLevel$100-150k                                                       8.319e+03
## IncomeLevel$20-50k                                                         8.376e+03
## IncomeLevel$50-100k                                                        8.372e+03
## BMI                                                                        8.343e+03
## CESD.10baseline                                                            8.368e+03
## SmokingStatusFormer Smoker                                                 8.390e+03
## SmokingStatusNever Smoked                                                  8.390e+03
## SmokingStatusOccasional Smoker                                             8.262e+03
## RelationshipstatusMarried                                                  8.390e+03
## RelationshipstatusSeparated                                                8.457e+03
## RelationshipstatusSingle                                                   8.379e+03
## RelationshipstatusWidowed                                                  8.396e+03
## LivingstatusAssisted Living                                                8.431e+03
## LivingstatusHouse                                                          8.370e+03
## LivingstatusOther                                                          8.285e+03
## AnxietyYes                                                                 8.363e+03
## MoodDisordYes                                                              8.364e+03
## Chronicconditions                                                          8.367e+03
## PASE_TOTALbaseline                                                         8.355e+03
## RVLT_Immediate_Normedbaseline                                              8.383e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     8.244e+03
## timefactor2:Age_sexFemales 65+                                             8.312e+03
## timefactor2:Age_sexMales 45-64                                             8.271e+03
## timefactor2:Age_sexMales 65+                                               8.337e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.463e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.465e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.466e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  8.270e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  8.205e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    8.270e+03
##                                                                           t value
## (Intercept)                                                                19.206
## timefactor2                                                                 3.426
## PandemicFU2 data collected before COVID-19                                  0.811
## Age_sexFemales 65+                                                         -0.454
## Age_sexMales 45-64                                                         -5.247
## Age_sexMales 65+                                                           -4.021
## EducationHigh School Diploma                                                2.735
## EducationLess than High School Diploma                                      3.597
## EducationSome College                                                       1.842
## EthnicityWhite                                                              2.448
## IncomeLevel>$150k                                                           2.716
## IncomeLevel$100-150k                                                        2.975
## IncomeLevel$20-50k                                                          1.415
## IncomeLevel$50-100k                                                         4.126
## BMI                                                                        -2.303
## CESD.10baseline                                                            -3.202
## SmokingStatusFormer Smoker                                                  1.166
## SmokingStatusNever Smoked                                                   1.937
## SmokingStatusOccasional Smoker                                             -0.113
## RelationshipstatusMarried                                                   2.838
## RelationshipstatusSeparated                                                 1.109
## RelationshipstatusSingle                                                    1.348
## RelationshipstatusWidowed                                                  -0.139
## LivingstatusAssisted Living                                                -1.902
## LivingstatusHouse                                                           0.474
## LivingstatusOther                                                          -0.949
## AnxietyYes                                                                  0.612
## MoodDisordYes                                                              -1.591
## Chronicconditions                                                          -1.883
## PASE_TOTALbaseline                                                          5.880
## RVLT_Immediate_Normedbaseline                                              42.933
## timefactor2:PandemicFU2 data collected before COVID-19                     -0.899
## timefactor2:Age_sexFemales 65+                                             -2.400
## timefactor2:Age_sexMales 45-64                                              0.645
## timefactor2:Age_sexMales 65+                                               -2.127
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.439
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              -0.026
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 0.366
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.298
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   0.445
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.389
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               0.000617
## PandemicFU2 data collected before COVID-19                                0.417244
## Age_sexFemales 65+                                                        0.649740
## Age_sexMales 45-64                                                        1.56e-07
## Age_sexMales 65+                                                          5.83e-05
## EducationHigh School Diploma                                              0.006256
## EducationLess than High School Diploma                                    0.000324
## EducationSome College                                                     0.065453
## EthnicityWhite                                                            0.014399
## IncomeLevel>$150k                                                         0.006619
## IncomeLevel$100-150k                                                      0.002940
## IncomeLevel$20-50k                                                        0.157006
## IncomeLevel$50-100k                                                       3.73e-05
## BMI                                                                       0.021330
## CESD.10baseline                                                           0.001371
## SmokingStatusFormer Smoker                                                0.243499
## SmokingStatusNever Smoked                                                 0.052758
## SmokingStatusOccasional Smoker                                            0.909795
## RelationshipstatusMarried                                                 0.004549
## RelationshipstatusSeparated                                               0.267331
## RelationshipstatusSingle                                                  0.177595
## RelationshipstatusWidowed                                                 0.889120
## LivingstatusAssisted Living                                               0.057266
## LivingstatusHouse                                                         0.635792
## LivingstatusOther                                                         0.342630
## AnxietyYes                                                                0.540833
## MoodDisordYes                                                             0.111633
## Chronicconditions                                                         0.059676
## PASE_TOTALbaseline                                                        4.26e-09
## RVLT_Immediate_Normedbaseline                                              < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                    0.368552
## timefactor2:Age_sexFemales 65+                                            0.016419
## timefactor2:Age_sexMales 45-64                                            0.518835
## timefactor2:Age_sexMales 65+                                              0.033486
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.660764
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.979026
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.714099
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.765955
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.655980
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.164935
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                   
## Age_sexFemales 65+                                                           
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          ***
## EducationHigh School Diploma                                              ** 
## EducationLess than High School Diploma                                    ***
## EducationSome College                                                     .  
## EthnicityWhite                                                            *  
## IncomeLevel>$150k                                                         ** 
## IncomeLevel$100-150k                                                      ** 
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       *  
## CESD.10baseline                                                           ** 
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                 .  
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                 ** 
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                               .  
## LivingstatusHouse                                                            
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         .  
## PASE_TOTALbaseline                                                        ***
## RVLT_Immediate_Normedbaseline                                             ***
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                            *  
## timefactor2:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                              *  
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

12.1.2) Estimated marginal means

Significantly lower RVLT immediate for males 65+

lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.80715 0.2268978 Inf 10.362437  11.25186
##  FU2 data collected before COVID-19 10.91454 0.2188850 Inf 10.485537  11.34355
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.21513 0.2277087 Inf 10.768825  11.66143
##  FU2 data collected before COVID-19 11.18638 0.2188665 Inf 10.757406  11.61535
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.72204 0.2506661 Inf 10.230742  11.21334
##  FU2 data collected before COVID-19 10.92976 0.2367831 Inf 10.465678  11.39385
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.63397 0.2523695 Inf 10.139338  11.12861
##  FU2 data collected before COVID-19 10.78353 0.2367328 Inf 10.319539  11.24751
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.08427 0.2186070 Inf  9.655806  10.51273
##  FU2 data collected before COVID-19 10.18672 0.2257134 Inf  9.744333  10.62911
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.59205 0.2188164 Inf 10.163174  11.02092
##  FU2 data collected before COVID-19 10.65391 0.2254293 Inf 10.212073  11.09574
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.10386 0.2473325 Inf  9.619100  10.58863
##  FU2 data collected before COVID-19 10.29267 0.2347117 Inf  9.832645  10.75270
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.09165 0.2492150 Inf  9.603193  10.58010
##  FU2 data collected before COVID-19 10.49855 0.2345335 Inf 10.038877  10.95823
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1073953 0.1323848 Inf  -0.811  0.4172
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   0.0287488 0.1339334 Inf   0.215  0.8300
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2077263 0.1867247 Inf  -1.112  0.2659
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1495543 0.1892579 Inf  -0.790  0.4294
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1024559 0.1340684 Inf  -0.764  0.4447
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0618604 0.1340137 Inf  -0.462  0.6444
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1888088 0.1787487 Inf  -1.056  0.2908
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.4069084 0.1809425 Inf  -2.249  0.0245
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1073953 0.1323848 Inf -0.3668647  0.1520741
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##   0.0287488 0.1339334 Inf -0.2337558  0.2912535
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.2077263 0.1867247 Inf -0.5737000  0.1582474
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1495543 0.1892579 Inf -0.5204929  0.2213844
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1024559 0.1340684 Inf -0.3652251  0.1603133
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.0618604 0.1340137 Inf -0.3245225  0.2008016
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1888088 0.1787487 Inf -0.5391498  0.1615323
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.4069084 0.1809425 Inf -0.7615492 -0.0522677
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

12.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_adj11 <- summary(lsmeans(modelRVLT_imm_adj11, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTimmediate_lsmeans_adj11$Time<-NA
RVLTimmediate_lsmeans_adj11$Time[RVLTimmediate_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj11$Time[RVLTimmediate_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

12.2) RVLT Delayed Recall

12.2.1) Model

modelRVLT_del_adj11<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +
                            (1|ID), data= Tracking.data_long_2)
summary(modelRVLT_del_adj11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +  
##     (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 84238.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9037 -0.5514 -0.0365  0.5079  3.9605 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.124    2.031   
##  Residual             6.928    2.632   
## Number of obs: 16282, groups:  ID, 8435
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                6.088e+00
## timefactor2                                                                8.002e-01
## PandemicFU2 data collected before COVID-19                                 2.208e-01
## Age_sexFemales 65+                                                         1.272e-01
## Age_sexMales 45-64                                                        -5.228e-01
## Age_sexMales 65+                                                          -4.201e-01
## EducationHigh School Diploma                                               2.951e-01
## EducationLess than High School Diploma                                     3.835e-01
## EducationSome College                                                      3.010e-01
## EthnicityWhite                                                             7.020e-01
## IncomeLevel>$150k                                                          4.284e-01
## IncomeLevel$100-150k                                                       3.492e-01
## IncomeLevel$20-50k                                                         1.516e-01
## IncomeLevel$50-100k                                                        4.574e-01
## BMI                                                                       -1.686e-02
## CESD.10baseline                                                           -2.066e-02
## SmokingStatusFormer Smoker                                                 7.169e-02
## SmokingStatusNever Smoked                                                  2.521e-01
## SmokingStatusOccasional Smoker                                             6.303e-02
## RelationshipstatusMarried                                                  9.608e-02
## RelationshipstatusSeparated                                               -4.161e-02
## RelationshipstatusSingle                                                   4.656e-02
## RelationshipstatusWidowed                                                 -1.809e-01
## LivingstatusAssisted Living                                               -1.073e+00
## LivingstatusHouse                                                          1.069e-01
## LivingstatusOther                                                         -2.054e-01
## AnxietyYes                                                                 1.123e-01
## MoodDisordYes                                                             -1.620e-01
## Chronicconditions                                                         -3.713e-02
## PASE_TOTALbaseline                                                         2.565e-03
## RVLT_Delayed_Normedbaseline                                                3.914e-01
## timefactor2:PandemicFU2 data collected before COVID-19                    -3.946e-01
## timefactor2:Age_sexFemales 65+                                            -6.193e-01
## timefactor2:Age_sexMales 45-64                                            -8.342e-02
## timefactor2:Age_sexMales 65+                                              -5.891e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.545e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             -3.201e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                6.066e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.918e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  6.574e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    4.066e-01
##                                                                           Std. Error
## (Intercept)                                                                3.428e-01
## timefactor2                                                                1.160e-01
## PandemicFU2 data collected before COVID-19                                 1.304e-01
## Age_sexFemales 65+                                                         1.848e-01
## Age_sexMales 45-64                                                         1.360e-01
## Age_sexMales 65+                                                           1.732e-01
## EducationHigh School Diploma                                               9.623e-02
## EducationLess than High School Diploma                                     1.368e-01
## EducationSome College                                                      1.194e-01
## EthnicityWhite                                                             1.916e-01
## IncomeLevel>$150k                                                          1.785e-01
## IncomeLevel$100-150k                                                       1.425e-01
## IncomeLevel$20-50k                                                         9.519e-02
## IncomeLevel$50-100k                                                        1.025e-01
## BMI                                                                        6.430e-03
## CESD.10baseline                                                            7.676e-03
## SmokingStatusFormer Smoker                                                 1.308e-01
## SmokingStatusNever Smoked                                                  1.362e-01
## SmokingStatusOccasional Smoker                                             2.579e-01
## RelationshipstatusMarried                                                  1.119e-01
## RelationshipstatusSeparated                                                2.203e-01
## RelationshipstatusSingle                                                   1.507e-01
## RelationshipstatusWidowed                                                  1.522e-01
## LivingstatusAssisted Living                                                4.533e-01
## LivingstatusHouse                                                          1.008e-01
## LivingstatusOther                                                          3.752e-01
## AnxietyYes                                                                 1.341e-01
## MoodDisordYes                                                              9.734e-02
## Chronicconditions                                                          1.558e-02
## PASE_TOTALbaseline                                                         4.437e-04
## RVLT_Delayed_Normedbaseline                                                8.294e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.475e-01
## timefactor2:Age_sexFemales 65+                                             2.026e-01
## timefactor2:Age_sexMales 45-64                                             1.510e-01
## timefactor2:Age_sexMales 65+                                               1.939e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.252e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.852e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.199e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.563e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.093e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.502e-01
##                                                                                   df
## (Intercept)                                                                8.838e+03
## timefactor2                                                                8.209e+03
## PandemicFU2 data collected before COVID-19                                 1.440e+04
## Age_sexFemales 65+                                                         1.393e+04
## Age_sexMales 45-64                                                         1.422e+04
## Age_sexMales 65+                                                           1.425e+04
## EducationHigh School Diploma                                               8.354e+03
## EducationLess than High School Diploma                                     8.546e+03
## EducationSome College                                                      8.272e+03
## EthnicityWhite                                                             8.389e+03
## IncomeLevel>$150k                                                          8.342e+03
## IncomeLevel$100-150k                                                       8.313e+03
## IncomeLevel$20-50k                                                         8.374e+03
## IncomeLevel$50-100k                                                        8.353e+03
## BMI                                                                        8.315e+03
## CESD.10baseline                                                            8.379e+03
## SmokingStatusFormer Smoker                                                 8.365e+03
## SmokingStatusNever Smoked                                                  8.364e+03
## SmokingStatusOccasional Smoker                                             8.283e+03
## RelationshipstatusMarried                                                  8.344e+03
## RelationshipstatusSeparated                                                8.467e+03
## RelationshipstatusSingle                                                   8.332e+03
## RelationshipstatusWidowed                                                  8.362e+03
## LivingstatusAssisted Living                                                8.459e+03
## LivingstatusHouse                                                          8.371e+03
## LivingstatusOther                                                          8.336e+03
## AnxietyYes                                                                 8.332e+03
## MoodDisordYes                                                              8.338e+03
## Chronicconditions                                                          8.349e+03
## PASE_TOTALbaseline                                                         8.350e+03
## RVLT_Delayed_Normedbaseline                                                8.356e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     8.139e+03
## timefactor2:Age_sexFemales 65+                                             8.266e+03
## timefactor2:Age_sexMales 45-64                                             8.184e+03
## timefactor2:Age_sexMales 65+                                               8.313e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.442e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.445e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.450e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  8.209e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  8.119e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    8.238e+03
##                                                                           t value
## (Intercept)                                                                17.760
## timefactor2                                                                 6.896
## PandemicFU2 data collected before COVID-19                                  1.693
## Age_sexFemales 65+                                                          0.689
## Age_sexMales 45-64                                                         -3.845
## Age_sexMales 65+                                                           -2.426
## EducationHigh School Diploma                                                3.067
## EducationLess than High School Diploma                                      2.804
## EducationSome College                                                       2.522
## EthnicityWhite                                                              3.664
## IncomeLevel>$150k                                                           2.399
## IncomeLevel$100-150k                                                        2.450
## IncomeLevel$20-50k                                                          1.592
## IncomeLevel$50-100k                                                         4.462
## BMI                                                                        -2.622
## CESD.10baseline                                                            -2.692
## SmokingStatusFormer Smoker                                                  0.548
## SmokingStatusNever Smoked                                                   1.852
## SmokingStatusOccasional Smoker                                              0.244
## RelationshipstatusMarried                                                   0.858
## RelationshipstatusSeparated                                                -0.189
## RelationshipstatusSingle                                                    0.309
## RelationshipstatusWidowed                                                  -1.189
## LivingstatusAssisted Living                                                -2.367
## LivingstatusHouse                                                           1.060
## LivingstatusOther                                                          -0.548
## AnxietyYes                                                                  0.837
## MoodDisordYes                                                              -1.664
## Chronicconditions                                                          -2.383
## PASE_TOTALbaseline                                                          5.782
## RVLT_Delayed_Normedbaseline                                                47.190
## timefactor2:PandemicFU2 data collected before COVID-19                     -2.675
## timefactor2:Age_sexFemales 65+                                             -3.057
## timefactor2:Age_sexMales 45-64                                             -0.553
## timefactor2:Age_sexMales 65+                                               -3.038
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.686
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              -0.173
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 0.276
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   1.138
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   0.314
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.625
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               5.75e-12
## PandemicFU2 data collected before COVID-19                                0.090473
## Age_sexFemales 65+                                                        0.491093
## Age_sexMales 45-64                                                        0.000121
## Age_sexMales 65+                                                          0.015285
## EducationHigh School Diploma                                              0.002172
## EducationLess than High School Diploma                                    0.005060
## EducationSome College                                                     0.011704
## EthnicityWhite                                                            0.000250
## IncomeLevel>$150k                                                         0.016449
## IncomeLevel$100-150k                                                      0.014313
## IncomeLevel$20-50k                                                        0.111404
## IncomeLevel$50-100k                                                       8.23e-06
## BMI                                                                       0.008747
## CESD.10baseline                                                           0.007119
## SmokingStatusFormer Smoker                                                0.583534
## SmokingStatusNever Smoked                                                 0.064115
## SmokingStatusOccasional Smoker                                            0.806911
## RelationshipstatusMarried                                                 0.390730
## RelationshipstatusSeparated                                               0.850198
## RelationshipstatusSingle                                                  0.757453
## RelationshipstatusWidowed                                                 0.234578
## LivingstatusAssisted Living                                               0.017957
## LivingstatusHouse                                                         0.288988
## LivingstatusOther                                                         0.584020
## AnxietyYes                                                                0.402442
## MoodDisordYes                                                             0.096149
## Chronicconditions                                                         0.017197
## PASE_TOTALbaseline                                                        7.66e-09
## RVLT_Delayed_Normedbaseline                                                < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                    0.007484
## timefactor2:Age_sexFemales 65+                                            0.002241
## timefactor2:Age_sexMales 45-64                                            0.580570
## timefactor2:Age_sexMales 65+                                              0.002391
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.492649
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.862807
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.782699
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.254977
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.753427
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.104196
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                .  
## Age_sexFemales 65+                                                           
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          *  
## EducationHigh School Diploma                                              ** 
## EducationLess than High School Diploma                                    ** 
## EducationSome College                                                     *  
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                         *  
## IncomeLevel$100-150k                                                      *  
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       ** 
## CESD.10baseline                                                           ** 
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                 .  
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                               *  
## LivingstatusHouse                                                            
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                             .  
## Chronicconditions                                                         *  
## PASE_TOTALbaseline                                                        ***
## RVLT_Delayed_Normedbaseline                                               ***
## timefactor2:PandemicFU2 data collected before COVID-19                    ** 
## timefactor2:Age_sexFemales 65+                                            ** 
## timefactor2:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                              ** 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

12.2.2) Estimated marginal means

No significant differences

lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.71833 0.2249975 Inf 10.277338  11.15931
##  FU2 data collected before COVID-19 10.93916 0.2170342 Inf 10.513781  11.36454
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.51857 0.2255048 Inf 11.076589  11.96055
##  FU2 data collected before COVID-19 11.34477 0.2170287 Inf 10.919397  11.77013
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.84558 0.2481783 Inf 10.359154  11.33199
##  FU2 data collected before COVID-19 11.22095 0.2346880 Inf 10.760971  11.68093
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.02656 0.2508985 Inf 10.534805  11.51831
##  FU2 data collected before COVID-19 11.29904 0.2347217 Inf 10.838998  11.75909
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.19548 0.2167819 Inf  9.770600  10.62037
##  FU2 data collected before COVID-19 10.38432 0.2235456 Inf  9.946174  10.82246
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.91231 0.2168276 Inf 10.487336  11.33728
##  FU2 data collected before COVID-19 10.77224 0.2235154 Inf 10.334154  11.21032
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.29818 0.2454416 Inf  9.817120  10.77923
##  FU2 data collected before COVID-19 10.57967 0.2330089 Inf 10.122985  11.03636
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.50936 0.2472205 Inf 10.024815  10.99390
##  FU2 data collected before COVID-19 10.80277 0.2325377 Inf 10.347005  11.25854
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2208354 0.1304385 Inf  -1.693  0.0905
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   0.1738044 0.1317497 Inf   1.319  0.1871
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3753766 0.1839559 Inf  -2.041  0.0413
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2724873 0.1875240 Inf  -1.453  0.1462
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1888302 0.1322031 Inf  -1.428  0.1532
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   0.1400740 0.1320982 Inf   1.060  0.2890
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2814974 0.1773064 Inf  -1.588  0.1124
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2934125 0.1790454 Inf  -1.639  0.1013
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.2208354 0.1304385 Inf -0.4764901  0.0348194
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##   0.1738044 0.1317497 Inf -0.0844203  0.4320290
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.3753766 0.1839559 Inf -0.7359235 -0.0148297
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.2724873 0.1875240 Inf -0.6400276  0.0950530
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1888302 0.1322031 Inf -0.4479436  0.0702832
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##   0.1400740 0.1320982 Inf -0.1188337  0.3989817
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.2814974 0.1773064 Inf -0.6290115  0.0660168
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.2934125 0.1790454 Inf -0.6443350  0.0575100
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

12.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_adj11 <- summary(lsmeans(modelRVLT_del_adj11, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTdelayed_lsmeans_adj11$Time<-NA
RVLTdelayed_lsmeans_adj11$Time[RVLTdelayed_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj11$Time[RVLTdelayed_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

12.3) Mental Alteration Test

12.3.1) Model

modelMAT_adj11<- lmer(MAT_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + MAT_Normedbaseline +
                      (1|ID), data= Tracking.data_long_2)
summary(modelMAT_adj11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## MAT_Normed ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +  
##     IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +  
##     Livingstatus + Anxiety + MoodDisord + Chronicconditions +  
##     PASE_TOTALbaseline + MAT_Normedbaseline + (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 79003.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4602 -0.5125 -0.0493  0.3954  4.6769 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.614    1.617   
##  Residual             7.688    2.773   
## Number of obs: 15353, groups:  ID, 8344
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                8.218e+00
## timefactor2                                                               -3.248e+00
## PandemicFU2 data collected before COVID-19                                -1.501e+00
## Age_sexFemales 65+                                                        -7.047e-01
## Age_sexMales 45-64                                                        -3.251e+00
## Age_sexMales 65+                                                          -3.317e+00
## EducationHigh School Diploma                                              -1.764e-02
## EducationLess than High School Diploma                                    -8.423e-02
## EducationSome College                                                     -1.048e-01
## EthnicityWhite                                                             8.453e-01
## IncomeLevel>$150k                                                          2.309e-01
## IncomeLevel$100-150k                                                       2.733e-01
## IncomeLevel$20-50k                                                         1.378e-01
## IncomeLevel$50-100k                                                        1.750e-01
## BMI                                                                       -1.162e-02
## CESD.10baseline                                                           -1.832e-02
## SmokingStatusFormer Smoker                                                 8.956e-02
## SmokingStatusNever Smoked                                                  3.153e-02
## SmokingStatusOccasional Smoker                                             1.909e-01
## RelationshipstatusMarried                                                 -5.330e-04
## RelationshipstatusSeparated                                               -9.028e-02
## RelationshipstatusSingle                                                   5.157e-01
## RelationshipstatusWidowed                                                 -1.177e-01
## LivingstatusAssisted Living                                               -3.390e-01
## LivingstatusHouse                                                         -2.149e-01
## LivingstatusOther                                                         -4.006e-01
## AnxietyYes                                                                 5.000e-02
## MoodDisordYes                                                              1.269e-01
## Chronicconditions                                                         -3.368e-02
## PASE_TOTALbaseline                                                        -3.947e-04
## MAT_Normedbaseline                                                         4.483e-01
## timefactor2:PandemicFU2 data collected before COVID-19                     1.647e+00
## timefactor2:Age_sexFemales 65+                                             4.769e-01
## timefactor2:Age_sexMales 45-64                                             3.291e+00
## timefactor2:Age_sexMales 65+                                               2.887e+00
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              3.811e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.624e+00
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.810e+00
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.312e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.765e+00
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   -1.593e+00
##                                                                           Std. Error
## (Intercept)                                                                3.272e-01
## timefactor2                                                                1.248e-01
## PandemicFU2 data collected before COVID-19                                 1.285e-01
## Age_sexFemales 65+                                                         1.859e-01
## Age_sexMales 45-64                                                         1.342e-01
## Age_sexMales 65+                                                           1.738e-01
## EducationHigh School Diploma                                               9.111e-02
## EducationLess than High School Diploma                                     1.311e-01
## EducationSome College                                                      1.123e-01
## EthnicityWhite                                                             1.823e-01
## IncomeLevel>$150k                                                          1.690e-01
## IncomeLevel$100-150k                                                       1.348e-01
## IncomeLevel$20-50k                                                         9.041e-02
## IncomeLevel$50-100k                                                        9.739e-02
## BMI                                                                        6.063e-03
## CESD.10baseline                                                            7.255e-03
## SmokingStatusFormer Smoker                                                 1.234e-01
## SmokingStatusNever Smoked                                                  1.285e-01
## SmokingStatusOccasional Smoker                                             2.434e-01
## RelationshipstatusMarried                                                  1.060e-01
## RelationshipstatusSeparated                                                2.078e-01
## RelationshipstatusSingle                                                   1.427e-01
## RelationshipstatusWidowed                                                  1.448e-01
## LivingstatusAssisted Living                                                4.298e-01
## LivingstatusHouse                                                          9.567e-02
## LivingstatusOther                                                          3.570e-01
## AnxietyYes                                                                 1.264e-01
## MoodDisordYes                                                              9.176e-02
## Chronicconditions                                                          1.480e-02
## PASE_TOTALbaseline                                                         4.183e-04
## MAT_Normedbaseline                                                         8.505e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.587e-01
## timefactor2:Age_sexFemales 65+                                             2.238e-01
## timefactor2:Age_sexMales 45-64                                             1.628e-01
## timefactor2:Age_sexMales 65+                                               2.144e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.262e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.833e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.201e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.817e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.262e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.755e-01
##                                                                                   df
## (Intercept)                                                                8.740e+03
## timefactor2                                                                7.722e+03
## PandemicFU2 data collected before COVID-19                                 1.461e+04
## Age_sexFemales 65+                                                         1.441e+04
## Age_sexMales 45-64                                                         1.446e+04
## Age_sexMales 65+                                                           1.451e+04
## EducationHigh School Diploma                                               8.100e+03
## EducationLess than High School Diploma                                     8.347e+03
## EducationSome College                                                      7.992e+03
## EthnicityWhite                                                             8.225e+03
## IncomeLevel>$150k                                                          8.088e+03
## IncomeLevel$100-150k                                                       8.064e+03
## IncomeLevel$20-50k                                                         8.162e+03
## IncomeLevel$50-100k                                                        8.115e+03
## BMI                                                                        8.003e+03
## CESD.10baseline                                                            8.063e+03
## SmokingStatusFormer Smoker                                                 8.051e+03
## SmokingStatusNever Smoked                                                  8.052e+03
## SmokingStatusOccasional Smoker                                             7.922e+03
## RelationshipstatusMarried                                                  8.160e+03
## RelationshipstatusSeparated                                                8.211e+03
## RelationshipstatusSingle                                                   8.128e+03
## RelationshipstatusWidowed                                                  8.211e+03
## LivingstatusAssisted Living                                                8.094e+03
## LivingstatusHouse                                                          8.172e+03
## LivingstatusOther                                                          8.166e+03
## AnxietyYes                                                                 8.068e+03
## MoodDisordYes                                                              8.043e+03
## Chronicconditions                                                          8.159e+03
## PASE_TOTALbaseline                                                         8.059e+03
## MAT_Normedbaseline                                                         8.157e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     7.648e+03
## timefactor2:Age_sexFemales 65+                                             8.109e+03
## timefactor2:Age_sexMales 45-64                                             7.716e+03
## timefactor2:Age_sexMales 65+                                               8.061e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.470e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.465e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.469e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  7.987e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  7.685e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    7.969e+03
##                                                                           t value
## (Intercept)                                                                25.117
## timefactor2                                                               -26.018
## PandemicFU2 data collected before COVID-19                                -11.678
## Age_sexFemales 65+                                                         -3.791
## Age_sexMales 45-64                                                        -24.226
## Age_sexMales 65+                                                          -19.084
## EducationHigh School Diploma                                               -0.194
## EducationLess than High School Diploma                                     -0.642
## EducationSome College                                                      -0.933
## EthnicityWhite                                                              4.638
## IncomeLevel>$150k                                                           1.367
## IncomeLevel$100-150k                                                        2.028
## IncomeLevel$20-50k                                                          1.524
## IncomeLevel$50-100k                                                         1.797
## BMI                                                                        -1.916
## CESD.10baseline                                                            -2.525
## SmokingStatusFormer Smoker                                                  0.726
## SmokingStatusNever Smoked                                                   0.245
## SmokingStatusOccasional Smoker                                              0.784
## RelationshipstatusMarried                                                  -0.005
## RelationshipstatusSeparated                                                -0.434
## RelationshipstatusSingle                                                    3.615
## RelationshipstatusWidowed                                                  -0.813
## LivingstatusAssisted Living                                                -0.789
## LivingstatusHouse                                                          -2.246
## LivingstatusOther                                                          -1.122
## AnxietyYes                                                                  0.396
## MoodDisordYes                                                               1.383
## Chronicconditions                                                          -2.275
## PASE_TOTALbaseline                                                         -0.944
## MAT_Normedbaseline                                                         52.717
## timefactor2:PandemicFU2 data collected before COVID-19                     10.378
## timefactor2:Age_sexFemales 65+                                              2.131
## timefactor2:Age_sexMales 45-64                                             20.216
## timefactor2:Age_sexMales 65+                                               13.461
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.169
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               8.863
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 8.222
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.821
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -7.800
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    -5.780
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                                < 2e-16
## PandemicFU2 data collected before COVID-19                                 < 2e-16
## Age_sexFemales 65+                                                        0.000151
## Age_sexMales 45-64                                                         < 2e-16
## Age_sexMales 65+                                                           < 2e-16
## EducationHigh School Diploma                                              0.846506
## EducationLess than High School Diploma                                    0.520609
## EducationSome College                                                     0.350710
## EthnicityWhite                                                            3.57e-06
## IncomeLevel>$150k                                                         0.171799
## IncomeLevel$100-150k                                                      0.042625
## IncomeLevel$20-50k                                                        0.127622
## IncomeLevel$50-100k                                                       0.072414
## BMI                                                                       0.055374
## CESD.10baseline                                                           0.011573
## SmokingStatusFormer Smoker                                                0.467980
## SmokingStatusNever Smoked                                                 0.806136
## SmokingStatusOccasional Smoker                                            0.432873
## RelationshipstatusMarried                                                 0.995989
## RelationshipstatusSeparated                                               0.663963
## RelationshipstatusSingle                                                  0.000302
## RelationshipstatusWidowed                                                 0.416131
## LivingstatusAssisted Living                                               0.430224
## LivingstatusHouse                                                         0.024738
## LivingstatusOther                                                         0.261936
## AnxietyYes                                                                0.692295
## MoodDisordYes                                                             0.166645
## Chronicconditions                                                         0.022931
## PASE_TOTALbaseline                                                        0.345432
## MAT_Normedbaseline                                                         < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                     < 2e-16
## timefactor2:Age_sexFemales 65+                                            0.033135
## timefactor2:Age_sexMales 45-64                                             < 2e-16
## timefactor2:Age_sexMales 65+                                               < 2e-16
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.866183
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              < 2e-16
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.411799
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 7.02e-15
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   7.73e-09
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                ***
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          ***
## EducationHigh School Diploma                                                 
## EducationLess than High School Diploma                                       
## EducationSome College                                                        
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                            
## IncomeLevel$100-150k                                                      *  
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       .  
## BMI                                                                       .  
## CESD.10baseline                                                           *  
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                  ***
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         *  
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         *  
## PASE_TOTALbaseline                                                           
## MAT_Normedbaseline                                                        ***
## timefactor2:PandemicFU2 data collected before COVID-19                    ***
## timefactor2:Age_sexFemales 65+                                            *  
## timefactor2:Age_sexMales 45-64                                            ***
## timefactor2:Age_sexMales 65+                                              ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             ***
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               ***
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 ***
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

12.3.2) Estimated marginal means

Significantly lower MAT score for males and females 65+

lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  12.710553 0.2153956 Inf 12.288385 13.132720
##  FU2 data collected before COVID-19 11.209445 0.2071594 Inf 10.803421 11.615470
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.462678 0.2155738 Inf  9.040162  9.885195
##  FU2 data collected before COVID-19  9.608991 0.2068806 Inf  9.203512 10.014469
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  12.005811 0.2419546 Inf 11.531589 12.480034
##  FU2 data collected before COVID-19 10.542814 0.2250573 Inf 10.101709 10.983918
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.234855 0.2424101 Inf  8.759740  9.709971
##  FU2 data collected before COVID-19  9.650495 0.2254384 Inf  9.208644 10.092346
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.459130 0.2072520 Inf  9.052924  9.865337
##  FU2 data collected before COVID-19  9.582490 0.2144732 Inf  9.162131 10.002850
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.502662 0.2068836 Inf  9.097177  9.908146
##  FU2 data collected before COVID-19  9.508845 0.2140541 Inf  9.089307  9.928384
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.394004 0.2386336 Inf  8.926291  9.861717
##  FU2 data collected before COVID-19  9.702846 0.2241882 Inf  9.263445 10.142247
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.032827 0.2410409 Inf  8.560395  9.505258
##  FU2 data collected before COVID-19  9.396384 0.2242718 Inf  8.956819  9.835949
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   1.5011073 0.1285441 Inf  11.678  <.0001
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1463123 0.1292796 Inf  -1.132  0.2577
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   1.4629978 0.1863424 Inf   7.851  <.0001
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.4156393 0.1886098 Inf  -2.204  0.0275
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1233600 0.1311306 Inf  -0.941  0.3468
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0061837 0.1303175 Inf  -0.047  0.9622
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3088420 0.1789088 Inf  -1.726  0.0843
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3635573 0.1827834 Inf  -1.989  0.0467
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##   1.5011073 0.1285441 Inf  1.2491654  1.7530493
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1463123 0.1292796 Inf -0.3996956  0.1070711
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##   1.4629978 0.1863424 Inf  1.0977735  1.8282222
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.4156393 0.1886098 Inf -0.7853077 -0.0459710
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1233600 0.1311306 Inf -0.3803714  0.1336513
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.0061837 0.1303175 Inf -0.2616012  0.2492339
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.3088420 0.1789088 Inf -0.6594967  0.0418127
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.3635573 0.1827834 Inf -0.7218062 -0.0053084
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

12.3.3) Graph of estimated marginal means

MAT_lsmeans_adj11 <- summary(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.

MAT_lsmeans_adj11$Time<-NA
MAT_lsmeans_adj11$Time[MAT_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj11$Time[MAT_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

12.4) Animal Fluency

12.4.1) Model

modelAnimals_adj11<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +
                           (1|ID), data= Tracking.data_long_2)
summary(modelAnimals_adj11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Animal_Fluency_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +  
##     (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 73825.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8810 -0.5507 -0.0181  0.5304  4.7952 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.383    1.544   
##  Residual             3.183    1.784   
## Number of obs: 16543, groups:  ID, 8454
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                4.157e+00
## timefactor2                                                                3.121e-01
## PandemicFU2 data collected before COVID-19                                 2.748e-01
## Age_sexFemales 65+                                                        -1.698e-01
## Age_sexMales 45-64                                                        -4.493e-02
## Age_sexMales 65+                                                          -1.221e-01
## EducationHigh School Diploma                                               2.332e-01
## EducationLess than High School Diploma                                     1.416e-01
## EducationSome College                                                      1.652e-01
## EthnicityWhite                                                             4.528e-01
## IncomeLevel>$150k                                                          1.195e-02
## IncomeLevel$100-150k                                                       6.033e-02
## IncomeLevel$20-50k                                                        -1.117e-01
## IncomeLevel$50-100k                                                        6.525e-02
## BMI                                                                       -8.184e-03
## CESD.10baseline                                                           -9.592e-03
## SmokingStatusFormer Smoker                                                 1.350e-01
## SmokingStatusNever Smoked                                                  1.667e-01
## SmokingStatusOccasional Smoker                                            -2.123e-02
## RelationshipstatusMarried                                                  4.821e-02
## RelationshipstatusSeparated                                                1.923e-01
## RelationshipstatusSingle                                                   6.596e-02
## RelationshipstatusWidowed                                                 -2.983e-02
## LivingstatusAssisted Living                                               -2.625e-01
## LivingstatusHouse                                                          1.521e-01
## LivingstatusOther                                                         -2.770e-01
## AnxietyYes                                                                 1.205e-01
## MoodDisordYes                                                              6.869e-02
## Chronicconditions                                                         -1.600e-02
## PASE_TOTALbaseline                                                         6.770e-04
## Animal_Fluency_Normedbaseline                                              5.268e-01
## timefactor2:PandemicFU2 data collected before COVID-19                    -1.060e-01
## timefactor2:Age_sexFemales 65+                                            -3.932e-01
## timefactor2:Age_sexMales 45-64                                            -1.889e-01
## timefactor2:Age_sexMales 65+                                              -5.325e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              7.517e-03
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             -2.616e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               -2.237e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.185e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.534e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    3.668e-01
##                                                                           Std. Error
## (Intercept)                                                                2.471e-01
## timefactor2                                                                7.828e-02
## PandemicFU2 data collected before COVID-19                                 9.212e-02
## Age_sexFemales 65+                                                         1.303e-01
## Age_sexMales 45-64                                                         9.596e-02
## Age_sexMales 65+                                                           1.217e-01
## EducationHigh School Diploma                                               6.926e-02
## EducationLess than High School Diploma                                     9.789e-02
## EducationSome College                                                      8.595e-02
## EthnicityWhite                                                             1.374e-01
## IncomeLevel>$150k                                                          1.285e-01
## IncomeLevel$100-150k                                                       1.027e-01
## IncomeLevel$20-50k                                                         6.844e-02
## IncomeLevel$50-100k                                                        7.376e-02
## BMI                                                                        4.628e-03
## CESD.10baseline                                                            5.524e-03
## SmokingStatusFormer Smoker                                                 9.417e-02
## SmokingStatusNever Smoked                                                  9.805e-02
## SmokingStatusOccasional Smoker                                             1.858e-01
## RelationshipstatusMarried                                                  8.058e-02
## RelationshipstatusSeparated                                                1.583e-01
## RelationshipstatusSingle                                                   1.085e-01
## RelationshipstatusWidowed                                                  1.094e-01
## LivingstatusAssisted Living                                                3.253e-01
## LivingstatusHouse                                                          7.252e-02
## LivingstatusOther                                                          2.688e-01
## AnxietyYes                                                                 9.650e-02
## MoodDisordYes                                                              7.012e-02
## Chronicconditions                                                          1.120e-02
## PASE_TOTALbaseline                                                         3.189e-04
## Animal_Fluency_Normedbaseline                                              7.167e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     9.946e-02
## timefactor2:Age_sexFemales 65+                                             1.356e-01
## timefactor2:Age_sexMales 45-64                                             1.018e-01
## timefactor2:Age_sexMales 65+                                               1.296e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.587e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.308e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.543e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.715e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.410e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.672e-01
##                                                                                   df
## (Intercept)                                                                8.772e+03
## timefactor2                                                                8.347e+03
## PandemicFU2 data collected before COVID-19                                 1.405e+04
## Age_sexFemales 65+                                                         1.354e+04
## Age_sexMales 45-64                                                         1.384e+04
## Age_sexMales 65+                                                           1.379e+04
## EducationHigh School Diploma                                               8.389e+03
## EducationLess than High School Diploma                                     8.479e+03
## EducationSome College                                                      8.336e+03
## EthnicityWhite                                                             8.308e+03
## IncomeLevel>$150k                                                          8.386e+03
## IncomeLevel$100-150k                                                       8.348e+03
## IncomeLevel$20-50k                                                         8.380e+03
## IncomeLevel$50-100k                                                        8.377e+03
## BMI                                                                        8.354e+03
## CESD.10baseline                                                            8.392e+03
## SmokingStatusFormer Smoker                                                 8.395e+03
## SmokingStatusNever Smoked                                                  8.392e+03
## SmokingStatusOccasional Smoker                                             8.319e+03
## RelationshipstatusMarried                                                  8.415e+03
## RelationshipstatusSeparated                                                8.450e+03
## RelationshipstatusSingle                                                   8.406e+03
## RelationshipstatusWidowed                                                  8.385e+03
## LivingstatusAssisted Living                                                8.402e+03
## LivingstatusHouse                                                          8.395e+03
## LivingstatusOther                                                          8.239e+03
## AnxietyYes                                                                 8.365e+03
## MoodDisordYes                                                              8.385e+03
## Chronicconditions                                                          8.359e+03
## PASE_TOTALbaseline                                                         8.367e+03
## Animal_Fluency_Normedbaseline                                              8.361e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     8.279e+03
## timefactor2:Age_sexFemales 65+                                             8.324e+03
## timefactor2:Age_sexMales 45-64                                             8.320e+03
## timefactor2:Age_sexMales 65+                                               8.350e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.402e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.408e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.406e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  8.276e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  8.251e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    8.285e+03
##                                                                           t value
## (Intercept)                                                                16.826
## timefactor2                                                                 3.988
## PandemicFU2 data collected before COVID-19                                  2.983
## Age_sexFemales 65+                                                         -1.303
## Age_sexMales 45-64                                                         -0.468
## Age_sexMales 65+                                                           -1.003
## EducationHigh School Diploma                                                3.367
## EducationLess than High School Diploma                                      1.447
## EducationSome College                                                       1.922
## EthnicityWhite                                                              3.296
## IncomeLevel>$150k                                                           0.093
## IncomeLevel$100-150k                                                        0.588
## IncomeLevel$20-50k                                                         -1.632
## IncomeLevel$50-100k                                                         0.885
## BMI                                                                        -1.768
## CESD.10baseline                                                            -1.736
## SmokingStatusFormer Smoker                                                  1.434
## SmokingStatusNever Smoked                                                   1.700
## SmokingStatusOccasional Smoker                                             -0.114
## RelationshipstatusMarried                                                   0.598
## RelationshipstatusSeparated                                                 1.215
## RelationshipstatusSingle                                                    0.608
## RelationshipstatusWidowed                                                  -0.273
## LivingstatusAssisted Living                                                -0.807
## LivingstatusHouse                                                           2.097
## LivingstatusOther                                                          -1.031
## AnxietyYes                                                                  1.249
## MoodDisordYes                                                               0.980
## Chronicconditions                                                          -1.429
## PASE_TOTALbaseline                                                          2.123
## Animal_Fluency_Normedbaseline                                              73.510
## timefactor2:PandemicFU2 data collected before COVID-19                     -1.066
## timefactor2:Age_sexFemales 65+                                             -2.900
## timefactor2:Age_sexMales 45-64                                             -1.856
## timefactor2:Age_sexMales 65+                                               -4.109
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.047
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              -2.001
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -1.450
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.691
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   1.088
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     2.194
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               6.73e-05
## PandemicFU2 data collected before COVID-19                                0.002858
## Age_sexFemales 65+                                                        0.192633
## Age_sexMales 45-64                                                        0.639637
## Age_sexMales 65+                                                          0.315683
## EducationHigh School Diploma                                              0.000762
## EducationLess than High School Diploma                                    0.147943
## EducationSome College                                                     0.054616
## EthnicityWhite                                                            0.000984
## IncomeLevel>$150k                                                         0.925913
## IncomeLevel$100-150k                                                      0.556871
## IncomeLevel$20-50k                                                        0.102627
## IncomeLevel$50-100k                                                       0.376371
## BMI                                                                       0.077033
## CESD.10baseline                                                           0.082546
## SmokingStatusFormer Smoker                                                0.151702
## SmokingStatusNever Smoked                                                 0.089131
## SmokingStatusOccasional Smoker                                            0.909018
## RelationshipstatusMarried                                                 0.549624
## RelationshipstatusSeparated                                               0.224342
## RelationshipstatusSingle                                                  0.543377
## RelationshipstatusWidowed                                                 0.785017
## LivingstatusAssisted Living                                               0.419680
## LivingstatusHouse                                                         0.035995
## LivingstatusOther                                                         0.302767
## AnxietyYes                                                                0.211862
## MoodDisordYes                                                             0.327272
## Chronicconditions                                                         0.153081
## PASE_TOTALbaseline                                                        0.033754
## Animal_Fluency_Normedbaseline                                              < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                    0.286512
## timefactor2:Age_sexFemales 65+                                            0.003744
## timefactor2:Age_sexMales 45-64                                            0.063458
## timefactor2:Age_sexMales 65+                                              4.02e-05
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.962216
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.045439
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.147142
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.489667
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.276693
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.028296
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                ** 
## Age_sexFemales 65+                                                           
## Age_sexMales 45-64                                                           
## Age_sexMales 65+                                                             
## EducationHigh School Diploma                                              ***
## EducationLess than High School Diploma                                       
## EducationSome College                                                     .  
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                            
## IncomeLevel$100-150k                                                         
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                          
## BMI                                                                       .  
## CESD.10baseline                                                           .  
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                 .  
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         *  
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                            
## PASE_TOTALbaseline                                                        *  
## Animal_Fluency_Normedbaseline                                             ***
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                            ** 
## timefactor2:Age_sexMales 45-64                                            .  
## timefactor2:Age_sexMales 65+                                              ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             *  
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

12.4.2) Estimated marginal means

Significantly lower animal fluency for males and females 65+

lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.907504 0.1608920 Inf  9.592161 10.222846
##  FU2 data collected before COVID-19 10.182323 0.1554464 Inf  9.877653 10.486992
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.219637 0.1613010 Inf  9.903493 10.535781
##  FU2 data collected before COVID-19 10.388444 0.1554599 Inf 10.083748 10.693139
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.737693 0.1770956 Inf  9.390592 10.084794
##  FU2 data collected before COVID-19 10.020029 0.1673864 Inf  9.691957 10.348100
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.656666 0.1781418 Inf  9.307514 10.005817
##  FU2 data collected before COVID-19  9.951504 0.1675738 Inf  9.623065 10.279942
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.862574 0.1551117 Inf  9.558561 10.166588
##  FU2 data collected before COVID-19  9.875764 0.1600020 Inf  9.562166 10.189362
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.985788 0.1552657 Inf  9.681473 10.290103
##  FU2 data collected before COVID-19 10.046322 0.1598905 Inf  9.732942 10.359701
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.785402 0.1748902 Inf  9.442624 10.128181
##  FU2 data collected before COVID-19  9.836489 0.1661219 Inf  9.510896 10.162082
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.565060 0.1760872 Inf  9.219935  9.910184
##  FU2 data collected before COVID-19  9.876957 0.1661718 Inf  9.551266 10.202647
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.27481878 0.09212404 Inf  -2.983  0.0029
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.16880671 0.09298717 Inf  -1.815  0.0695
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.28233582 0.12944447 Inf  -2.181  0.0292
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.29483786 0.13123052 Inf  -2.247  0.0247
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.01318983 0.09317313 Inf  -0.142  0.8874
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.06053375 0.09322035 Inf  -0.649  0.5161
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.05108655 0.12398229 Inf  -0.412  0.6803
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.31189693 0.12559983 Inf  -2.483  0.0130
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.27481878 0.09212404 Inf -0.4553786 -0.09425898
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.16880671 0.09298717 Inf -0.3510582  0.01344479
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.28233582 0.12944447 Inf -0.5360423 -0.02862933
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.29483786 0.13123052 Inf -0.5520450 -0.03763076
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.01318983 0.09317313 Inf -0.1958058  0.16942616
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.06053375 0.09322035 Inf -0.2432423  0.12217477
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.05108655 0.12398229 Inf -0.2940874  0.19191427
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.31189693 0.12559983 Inf -0.5580681 -0.06572578
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

12.4.3) Graph of estimated marginal means

Animals_lsmeans_adj11 <- summary(lsmeans(modelAnimals_adj11, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.

Animals_lsmeans_adj11$Time<-NA
Animals_lsmeans_adj11$Time[Animals_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj11$Time[Animals_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

13) Physical Activity Results

13.1) Full model unadjusted for baseline

13.1.1) Model

modelPASE_1<- lmer(PASE_TOTAL~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
                         (1|ID), data= Tracking.data_long)
summary(modelPASE_1)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PASE_TOTAL ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 142465.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5047 -0.5346 -0.0521  0.4931  6.6277 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 1695     41.17   
##  Residual             2574     50.73   
## Number of obs: 12799, groups:  ID, 8921
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             3.572e+02  8.182e+00
## timefactor2                                            -3.314e+00  2.017e+00
## timefactor3                                            -2.242e+01  2.091e+00
## PandemicFU2 data collected before COVID-19             -7.520e+00  1.449e+00
## Age                                                    -3.376e+00  7.809e-02
## SexM                                                    2.195e+01  1.423e+00
## EducationHigh School Diploma                            2.466e-01  2.050e+00
## EducationLess than High School Diploma                  3.812e+00  2.940e+00
## EducationSome College                                   8.714e+00  2.514e+00
## EthnicityWhite                                          4.642e+00  3.979e+00
## IncomeLevel>$150k                                       3.521e+01  3.716e+00
## IncomeLevel$100-150k                                    3.598e+01  2.986e+00
## IncomeLevel$20-50k                                      1.608e+01  2.017e+00
## IncomeLevel$50-100k                                     2.575e+01  2.154e+00
## BMI                                                    -6.881e-01  1.379e-01
## CESD.20.1                                              -4.974e-01  1.627e-01
## SmokingStatusFormer Smoker                              5.234e-01  2.805e+00
## SmokingStatusNever Smoked                               5.746e+00  2.915e+00
## SmokingStatusOccasional Smoker                          2.039e+00  5.482e+00
## RelationshipstatusMarried                              -8.902e+00  2.353e+00
## RelationshipstatusSeparated                            -2.665e+00  4.649e+00
## RelationshipstatusSingle                               -1.529e+01  3.192e+00
## RelationshipstatusWidowed                              -4.304e+00  3.221e+00
## LivingstatusAssisted Living                            -1.978e-01  9.693e+00
## LivingstatusHouse                                       2.472e+01  2.115e+00
## LivingstatusOther                                       2.751e+01  7.935e+00
## AnxietyYes                                             -6.175e+00  2.831e+00
## MoodDisordYes                                          -8.554e+00  2.049e+00
## Chronicconditions                                      -1.757e+00  3.310e-01
## timefactor2:PandemicFU2 data collected before COVID-19 -4.649e+00  2.799e+00
## timefactor3:PandemicFU2 data collected before COVID-19  1.070e+01  2.719e+00
##                                                                df t value
## (Intercept)                                             8.771e+03  43.656
## timefactor2                                             7.076e+03  -1.643
## timefactor3                                             7.083e+03 -10.723
## PandemicFU2 data collected before COVID-19              1.172e+04  -5.190
## Age                                                     8.558e+03 -43.238
## SexM                                                    8.482e+03  15.420
## EducationHigh School Diploma                            8.767e+03   0.120
## EducationLess than High School Diploma                  9.273e+03   1.297
## EducationSome College                                   8.396e+03   3.466
## EthnicityWhite                                          8.726e+03   1.167
## IncomeLevel>$150k                                       8.286e+03   9.474
## IncomeLevel$100-150k                                    8.338e+03  12.048
## IncomeLevel$20-50k                                      8.728e+03   7.971
## IncomeLevel$50-100k                                     8.600e+03  11.952
## BMI                                                     8.990e+03  -4.990
## CESD.20.1                                               8.642e+03  -3.057
## SmokingStatusFormer Smoker                              8.971e+03   0.187
## SmokingStatusNever Smoked                               8.941e+03   1.971
## SmokingStatusOccasional Smoker                          8.307e+03   0.372
## RelationshipstatusMarried                               8.422e+03  -3.784
## RelationshipstatusSeparated                             8.459e+03  -0.573
## RelationshipstatusSingle                                8.543e+03  -4.791
## RelationshipstatusWidowed                               8.609e+03  -1.336
## LivingstatusAssisted Living                             8.192e+03  -0.020
## LivingstatusHouse                                       8.476e+03  11.692
## LivingstatusOther                                       9.074e+03   3.468
## AnxietyYes                                              8.569e+03  -2.181
## MoodDisordYes                                           8.431e+03  -4.174
## Chronicconditions                                       8.478e+03  -5.308
## timefactor2:PandemicFU2 data collected before COVID-19  7.033e+03  -1.661
## timefactor3:PandemicFU2 data collected before COVID-19  7.006e+03   3.937
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            0.100471    
## timefactor3                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             2.14e-07 ***
## Age                                                     < 2e-16 ***
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                           0.904230    
## EducationLess than High School Diploma                 0.194722    
## EducationSome College                                  0.000532 ***
## EthnicityWhite                                         0.243431    
## IncomeLevel>$150k                                       < 2e-16 ***
## IncomeLevel$100-150k                                    < 2e-16 ***
## IncomeLevel$20-50k                                     1.78e-15 ***
## IncomeLevel$50-100k                                     < 2e-16 ***
## BMI                                                    6.14e-07 ***
## CESD.20.1                                              0.002243 ** 
## SmokingStatusFormer Smoker                             0.851996    
## SmokingStatusNever Smoked                              0.048740 *  
## SmokingStatusOccasional Smoker                         0.709947    
## RelationshipstatusMarried                              0.000156 ***
## RelationshipstatusSeparated                            0.566408    
## RelationshipstatusSingle                               1.69e-06 ***
## RelationshipstatusWidowed                              0.181504    
## LivingstatusAssisted Living                            0.983718    
## LivingstatusHouse                                       < 2e-16 ***
## LivingstatusOther                                      0.000528 ***
## AnxietyYes                                             0.029176 *  
## MoodDisordYes                                          3.02e-05 ***
## Chronicconditions                                      1.14e-07 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.096708 .  
## timefactor3:PandemicFU2 data collected before COVID-19 8.34e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

13.1.2) Estimated marginal means

Significant differences for males 45-64 and 65+, whereby post-pandemic males performed worse

lsmeans(modelPASE_1, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  166.0147 4.344428 Inf  157.4997  174.5296
##  FU2 data collected before COVID-19 158.4949 4.352155 Inf  149.9649  167.0250
## 
## timefactor = 2:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  162.7005 4.650760 Inf  153.5852  171.8159
##  FU2 data collected before COVID-19 150.5317 4.645490 Inf  141.4267  159.6367
## 
## timefactor = 3:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  143.5907 4.677721 Inf  134.4226  152.7589
##  FU2 data collected before COVID-19 146.7748 4.558379 Inf  137.8406  155.7091
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelPASE_1, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df z.ratio p.value
##   7.519720 1.448962 Inf   5.190  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df z.ratio p.value
##  12.168875 2.707676 Inf   4.494  <.0001
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df z.ratio p.value
##  -3.184105 2.624720 Inf  -1.213  0.2251
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelPASE_1, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df asymp.LCL asymp.UCL
##   7.519720 1.448962 Inf  4.679806 10.359634
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df asymp.LCL asymp.UCL
##  12.168875 2.707676 Inf  6.861928 17.475822
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df asymp.LCL asymp.UCL
##  -3.184105 2.624720 Inf -8.328461  1.960251
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

13.1.3) Graph of estimated marginal means

PASE_lsmeans_1 <- summary(lsmeans(modelPASE_1, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.

PASE_lsmeans_1$Time<-NA
PASE_lsmeans_1$Time[PASE_lsmeans_1$timefactor==1]<-"Baseline"
PASE_lsmeans_1$Time[PASE_lsmeans_1$timefactor==2]<-"Follow-up 1"
PASE_lsmeans_1$Time[PASE_lsmeans_1$timefactor==3]<-"Follow-up 2"
ggplot(PASE_lsmeans_1, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + 
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

13.1.4) Planned contrasts

lsmeans.PASEtrun <- lsmeans(modelPASE_1, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.PASEtrun,list(c1st,c2nd,c3rd),by=NULL)

##  contrast                                            estimate       SE  df
##  structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L))   4.649155 2.798586 Inf
##  structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) -15.352980 3.463034 Inf
##  structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) -10.703825 2.719007 Inf
##  z.ratio p.value
##    1.661  0.0967
##   -4.433  <.0001
##   -3.937  0.0001
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

13.2) Full model adjusted for baseline

13.2.1) Model

modelPASE_2<- lmer(PASE_TOTAL ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= Tracking.data_long_2)
summary(modelPASE_2)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PASE_TOTAL ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 41379.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6919 -0.5399 -0.0547  0.5147  4.6443 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 1120     33.47   
##  Residual             2268     47.62   
## Number of obs: 3793, groups:  ID, 3084
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             227.04329   14.48019
## timefactor2                                             -19.57979    2.64778
## PandemicFU2 data collected before COVID-19               -8.30427    2.75447
## Age                                                      -2.28226    0.13554
## SexM                                                     19.08687    2.26456
## EducationHigh School Diploma                            -10.28018    3.35721
## EducationLess than High School Diploma                   -2.60637    5.33767
## EducationSome College                                     5.82881    3.88524
## EthnicityWhite                                           15.65811    6.50262
## IncomeLevel>$150k                                        10.13841    5.72612
## IncomeLevel$100-150k                                     13.35465    4.63478
## IncomeLevel$20-50k                                        6.55322    3.32002
## IncomeLevel$50-100k                                      10.32450    3.48386
## BMI                                                      -0.44277    0.23659
## CESD.10baseline                                           0.17110    0.26176
## SmokingStatusFormer Smoker                               -2.90890    4.82660
## SmokingStatusNever Smoked                                -0.02248    4.98545
## SmokingStatusOccasional Smoker                           -7.94910    8.41014
## RelationshipstatusMarried                                -3.59607    3.63034
## RelationshipstatusSeparated                               2.93953    7.20676
## RelationshipstatusSingle                                 -6.34248    5.06510
## RelationshipstatusWidowed                                -0.74498    5.15533
## LivingstatusAssisted Living                             -13.91674   14.31955
## LivingstatusHouse                                        13.00541    3.33789
## LivingstatusOther                                        34.09675   13.60157
## AnxietyYes                                               -2.17396    4.50546
## MoodDisordYes                                            -9.86520    3.18407
## Chronicconditions                                        -1.60979    0.52194
## PASE_TOTALbaseline                                        0.36667    0.01506
## timefactor2:PandemicFU2 data collected before COVID-19   15.51764    3.52774
##                                                                df t value
## (Intercept)                                            2954.43057  15.680
## timefactor2                                            2275.73813  -7.395
## PandemicFU2 data collected before COVID-19             3750.05357  -3.015
## Age                                                    2936.56481 -16.838
## SexM                                                   2908.58872   8.429
## EducationHigh School Diploma                           2969.33237  -3.062
## EducationLess than High School Diploma                 2949.45310  -0.488
## EducationSome College                                  2890.33885   1.500
## EthnicityWhite                                         3166.30903   2.408
## IncomeLevel>$150k                                      2905.39647   1.771
## IncomeLevel$100-150k                                   2902.75839   2.881
## IncomeLevel$20-50k                                     2829.44662   1.974
## IncomeLevel$50-100k                                    2840.93470   2.964
## BMI                                                    2952.58643  -1.871
## CESD.10baseline                                        2907.41624   0.654
## SmokingStatusFormer Smoker                             2912.14471  -0.603
## SmokingStatusNever Smoked                              2910.19409  -0.005
## SmokingStatusOccasional Smoker                         2814.23613  -0.945
## RelationshipstatusMarried                              2953.60642  -0.991
## RelationshipstatusSeparated                            2818.72500   0.408
## RelationshipstatusSingle                               2850.65384  -1.252
## RelationshipstatusWidowed                              2905.49242  -0.145
## LivingstatusAssisted Living                            3009.03622  -0.972
## LivingstatusHouse                                      2927.99248   3.896
## LivingstatusOther                                      3066.72319   2.507
## AnxietyYes                                             2935.07572  -0.483
## MoodDisordYes                                          2943.60783  -3.098
## Chronicconditions                                      2856.90903  -3.084
## PASE_TOTALbaseline                                     2899.10049  24.341
## timefactor2:PandemicFU2 data collected before COVID-19 2199.00680   4.399
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            1.98e-13 ***
## PandemicFU2 data collected before COVID-19              0.00259 ** 
## Age                                                     < 2e-16 ***
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                            0.00222 ** 
## EducationLess than High School Diploma                  0.62538    
## EducationSome College                                   0.13366    
## EthnicityWhite                                          0.01610 *  
## IncomeLevel>$150k                                       0.07674 .  
## IncomeLevel$100-150k                                    0.00399 ** 
## IncomeLevel$20-50k                                      0.04850 *  
## IncomeLevel$50-100k                                     0.00307 ** 
## BMI                                                     0.06138 .  
## CESD.10baseline                                         0.51340    
## SmokingStatusFormer Smoker                              0.54677    
## SmokingStatusNever Smoked                               0.99640    
## SmokingStatusOccasional Smoker                          0.34465    
## RelationshipstatusMarried                               0.32198    
## RelationshipstatusSeparated                             0.68339    
## RelationshipstatusSingle                                0.21060    
## RelationshipstatusWidowed                               0.88511    
## LivingstatusAssisted Living                             0.33119    
## LivingstatusHouse                                      9.99e-05 ***
## LivingstatusOther                                       0.01223 *  
## AnxietyYes                                              0.62947    
## MoodDisordYes                                           0.00196 ** 
## Chronicconditions                                       0.00206 ** 
## PASE_TOTALbaseline                                      < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 1.14e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

13.2.2) Estimated marginal means

lsmeans(modelPASE_2, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  159.7396 7.176628 Inf  145.6737  173.8055
##  FU2 data collected before COVID-19 151.4353 7.141678 Inf  137.4379  165.4327
## 
## timefactor = 2:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  140.1598 7.146012 Inf  126.1539  154.1657
##  FU2 data collected before COVID-19 147.3732 7.099894 Inf  133.4576  161.2887
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelPASE_2, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df z.ratio p.value
##   8.304269 2.754465 Inf   3.015  0.0026
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df z.ratio p.value
##  -7.213373 2.668837 Inf  -2.703  0.0069
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelPASE_2, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df  asymp.LCL asymp.UCL
##   8.304269 2.754465 Inf   2.905616 13.702921
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df  asymp.LCL asymp.UCL
##  -7.213373 2.668837 Inf -12.444198 -1.982547
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

13.2.3) Graph of estimated marginal means

PASE_lsmeans_2 <- summary(lsmeans(modelPASE_2, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.

PASE_lsmeans_2$Time<-NA
PASE_lsmeans_2$Time[PASE_lsmeans_2$timefactor==1]<-"Follow-up 1"
PASE_lsmeans_2$Time[PASE_lsmeans_2$timefactor==2]<-"Follow-up 2"
ggplot(PASE_lsmeans_2, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

13.2.4) Planned contrasts

lsmeans.PASE2trun <- lsmeans(modelPASE_2, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.

contrast(lsmeans.PASE2trun,list(c4th),by=NULL)

##  contrast                                     estimate      SE  df z.ratio
##  structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -15.51764 3.52774 Inf  -4.399
##  p.value
##   <.0001
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

13.3) Full model unadjusted for baseline (age and sex differences)

13.3.1) Model

modelPASE_3<- lmer(PASE_TOTAL ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
                    (1|ID), data= Tracking.data_long)
summary(modelPASE_3)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## PASE_TOTAL ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +  
##     IncomeLevel + BMI + CESD.20.1 + SmokingStatus + Relationshipstatus +  
##     Livingstatus + Anxiety + MoodDisord + Chronicconditions +      (1 | ID)
##    Data: Tracking.data_long
## 
## REML criterion at convergence: 143056.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5215 -0.5361 -0.0443  0.4883  6.0745 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2032     45.07   
##  Residual             2545     50.45   
## Number of obs: 12799, groups:  ID, 8921
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                 167.7452
## timefactor2                                                                  -0.5983
## timefactor3                                                                 -26.3636
## PandemicFU2 data collected before COVID-19                                  -11.2140
## Age_sexFemales 65+                                                          -45.8363
## Age_sexMales 45-64                                                           24.5914
## Age_sexMales 65+                                                            -41.1588
## EducationHigh School Diploma                                                 -0.8631
## EducationLess than High School Diploma                                       -0.7466
## EducationSome College                                                         6.0380
## EthnicityWhite                                                                0.4625
## IncomeLevel>$150k                                                            40.8591
## IncomeLevel$100-150k                                                         42.0589
## IncomeLevel$20-50k                                                           16.8643
## IncomeLevel$50-100k                                                          29.7152
## BMI                                                                          -0.4639
## CESD.20.1                                                                    -0.2710
## SmokingStatusFormer Smoker                                                   -3.1718
## SmokingStatusNever Smoked                                                     2.9083
## SmokingStatusOccasional Smoker                                                1.6958
## RelationshipstatusMarried                                                    -7.2289
## RelationshipstatusSeparated                                                   1.8509
## RelationshipstatusSingle                                                    -12.2730
## RelationshipstatusWidowed                                                   -17.2244
## LivingstatusAssisted Living                                                 -12.5590
## LivingstatusHouse                                                            28.8222
## LivingstatusOther                                                            32.8348
## AnxietyYes                                                                   -2.5781
## MoodDisordYes                                                                -6.9188
## Chronicconditions                                                            -3.2665
## timefactor2:PandemicFU2 data collected before COVID-19                       -9.4841
## timefactor3:PandemicFU2 data collected before COVID-19                       12.8291
## timefactor2:Age_sexFemales 65+                                              -10.0334
## timefactor3:Age_sexFemales 65+                                                0.3101
## timefactor2:Age_sexMales 45-64                                               -5.7577
## timefactor3:Age_sexMales 45-64                                                8.9975
## timefactor2:Age_sexMales 65+                                                  4.5436
## timefactor3:Age_sexMales 65+                                                 -1.2600
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                 4.8711
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                -4.2464
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                   8.1284
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    13.4949
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+     0.3230
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    12.0617
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    -9.5731
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      -4.8563
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      11.0099
##                                                                           Std. Error
## (Intercept)                                                                   7.0918
## timefactor2                                                                   3.6104
## timefactor3                                                                   3.6841
## PandemicFU2 data collected before COVID-19                                    2.6073
## Age_sexFemales 65+                                                            3.6999
## Age_sexMales 45-64                                                            2.7106
## Age_sexMales 65+                                                              3.4435
## EducationHigh School Diploma                                                  2.1395
## EducationLess than High School Diploma                                        3.0637
## EducationSome College                                                         2.6263
## EthnicityWhite                                                                4.1509
## IncomeLevel>$150k                                                             3.8779
## IncomeLevel$100-150k                                                          3.1104
## IncomeLevel$20-50k                                                            2.1070
## IncomeLevel$50-100k                                                           2.2476
## BMI                                                                           0.1436
## CESD.20.1                                                                     0.1696
## SmokingStatusFormer Smoker                                                    2.9232
## SmokingStatusNever Smoked                                                     3.0407
## SmokingStatusOccasional Smoker                                                5.7271
## RelationshipstatusMarried                                                     2.4628
## RelationshipstatusSeparated                                                   4.8541
## RelationshipstatusSingle                                                      3.3341
## RelationshipstatusWidowed                                                     3.3483
## LivingstatusAssisted Living                                                  10.1264
## LivingstatusHouse                                                             2.2055
## LivingstatusOther                                                             8.2758
## AnxietyYes                                                                    2.9531
## MoodDisordYes                                                                 2.1419
## Chronicconditions                                                             0.3416
## timefactor2:PandemicFU2 data collected before COVID-19                        4.7623
## timefactor3:PandemicFU2 data collected before COVID-19                        4.5913
## timefactor2:Age_sexFemales 65+                                                6.6896
## timefactor3:Age_sexFemales 65+                                                6.9388
## timefactor2:Age_sexMales 45-64                                                4.8472
## timefactor3:Age_sexMales 45-64                                                4.9372
## timefactor2:Age_sexMales 65+                                                  6.2276
## timefactor3:Age_sexMales 65+                                                  6.7673
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                 4.5033
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                 3.7024
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                   4.3782
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+     8.5848
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+     8.4820
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64     7.0564
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64     6.7285
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+       8.3550
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+       8.4192
##                                                                                   df
## (Intercept)                                                                9169.0045
## timefactor2                                                                6631.4472
## timefactor3                                                                6592.9808
## PandemicFU2 data collected before COVID-19                                11653.5061
## Age_sexFemales 65+                                                        11445.8943
## Age_sexMales 45-64                                                        11588.9293
## Age_sexMales 65+                                                          11493.7519
## EducationHigh School Diploma                                               8879.4010
## EducationLess than High School Diploma                                     9312.3854
## EducationSome College                                                      8545.2622
## EthnicityWhite                                                             8828.2245
## IncomeLevel>$150k                                                          8440.8765
## IncomeLevel$100-150k                                                       8484.3413
## IncomeLevel$20-50k                                                         8843.3563
## IncomeLevel$50-100k                                                        8731.4268
## BMI                                                                        9082.8962
## CESD.20.1                                                                  8768.7720
## SmokingStatusFormer Smoker                                                 9059.6409
## SmokingStatusNever Smoked                                                  9032.1957
## SmokingStatusOccasional Smoker                                             8465.0517
## RelationshipstatusMarried                                                  8590.9892
## RelationshipstatusSeparated                                                8603.4254
## RelationshipstatusSingle                                                   8687.9964
## RelationshipstatusWidowed                                                  8726.3521
## LivingstatusAssisted Living                                                8356.3302
## LivingstatusHouse                                                          8608.7322
## LivingstatusOther                                                          9155.5462
## AnxietyYes                                                                 8698.9296
## MoodDisordYes                                                              8572.1997
## Chronicconditions                                                          8651.3642
## timefactor2:PandemicFU2 data collected before COVID-19                     6628.3066
## timefactor3:PandemicFU2 data collected before COVID-19                     6502.7641
## timefactor2:Age_sexFemales 65+                                             6871.8670
## timefactor3:Age_sexFemales 65+                                             7005.1325
## timefactor2:Age_sexMales 45-64                                             6764.5832
## timefactor3:Age_sexMales 45-64                                             6740.1311
## timefactor2:Age_sexMales 65+                                               6643.0545
## timefactor3:Age_sexMales 65+                                               6661.6252
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             11658.0478
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             11634.4681
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               11602.3606
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  6796.9877
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  6920.7602
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  6704.0699
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  6620.6272
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    6688.7036
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    6673.4271
##                                                                           t value
## (Intercept)                                                                23.653
## timefactor2                                                                -0.166
## timefactor3                                                                -7.156
## PandemicFU2 data collected before COVID-19                                 -4.301
## Age_sexFemales 65+                                                        -12.388
## Age_sexMales 45-64                                                          9.072
## Age_sexMales 65+                                                          -11.953
## EducationHigh School Diploma                                               -0.403
## EducationLess than High School Diploma                                     -0.244
## EducationSome College                                                       2.299
## EthnicityWhite                                                              0.111
## IncomeLevel>$150k                                                          10.537
## IncomeLevel$100-150k                                                       13.522
## IncomeLevel$20-50k                                                          8.004
## IncomeLevel$50-100k                                                        13.221
## BMI                                                                        -3.231
## CESD.20.1                                                                  -1.597
## SmokingStatusFormer Smoker                                                 -1.085
## SmokingStatusNever Smoked                                                   0.956
## SmokingStatusOccasional Smoker                                              0.296
## RelationshipstatusMarried                                                  -2.935
## RelationshipstatusSeparated                                                 0.381
## RelationshipstatusSingle                                                   -3.681
## RelationshipstatusWidowed                                                  -5.144
## LivingstatusAssisted Living                                                -1.240
## LivingstatusHouse                                                          13.069
## LivingstatusOther                                                           3.968
## AnxietyYes                                                                 -0.873
## MoodDisordYes                                                              -3.230
## Chronicconditions                                                          -9.562
## timefactor2:PandemicFU2 data collected before COVID-19                     -1.991
## timefactor3:PandemicFU2 data collected before COVID-19                      2.794
## timefactor2:Age_sexFemales 65+                                             -1.500
## timefactor3:Age_sexFemales 65+                                              0.045
## timefactor2:Age_sexMales 45-64                                             -1.188
## timefactor3:Age_sexMales 45-64                                              1.822
## timefactor2:Age_sexMales 65+                                                0.730
## timefactor3:Age_sexMales 65+                                               -0.186
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               1.082
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              -1.147
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 1.857
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   1.572
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.038
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   1.709
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -1.423
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    -0.581
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.308
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               0.868375
## timefactor3                                                               9.21e-13
## PandemicFU2 data collected before COVID-19                                1.71e-05
## Age_sexFemales 65+                                                         < 2e-16
## Age_sexMales 45-64                                                         < 2e-16
## Age_sexMales 65+                                                           < 2e-16
## EducationHigh School Diploma                                              0.686668
## EducationLess than High School Diploma                                    0.807462
## EducationSome College                                                     0.021527
## EthnicityWhite                                                            0.911280
## IncomeLevel>$150k                                                          < 2e-16
## IncomeLevel$100-150k                                                       < 2e-16
## IncomeLevel$20-50k                                                        1.36e-15
## IncomeLevel$50-100k                                                        < 2e-16
## BMI                                                                       0.001239
## CESD.20.1                                                                 0.110193
## SmokingStatusFormer Smoker                                                0.277932
## SmokingStatusNever Smoked                                                 0.338860
## SmokingStatusOccasional Smoker                                            0.767163
## RelationshipstatusMarried                                                 0.003341
## RelationshipstatusSeparated                                               0.702977
## RelationshipstatusSingle                                                  0.000234
## RelationshipstatusWidowed                                                 2.74e-07
## LivingstatusAssisted Living                                               0.214927
## LivingstatusHouse                                                          < 2e-16
## LivingstatusOther                                                         7.32e-05
## AnxietyYes                                                                0.382680
## MoodDisordYes                                                             0.001241
## Chronicconditions                                                          < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                    0.046467
## timefactor3:PandemicFU2 data collected before COVID-19                    0.005218
## timefactor2:Age_sexFemales 65+                                            0.133699
## timefactor3:Age_sexFemales 65+                                            0.964360
## timefactor2:Age_sexMales 45-64                                            0.234937
## timefactor3:Age_sexMales 45-64                                            0.068438
## timefactor2:Age_sexMales 65+                                              0.465668
## timefactor3:Age_sexMales 65+                                              0.852301
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.279415
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.251440
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.063395
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.116007
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.969627
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.087438
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.154853
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.561095
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.191016
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                                  
## timefactor3                                                               ***
## PandemicFU2 data collected before COVID-19                                ***
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          ***
## EducationHigh School Diploma                                                 
## EducationLess than High School Diploma                                       
## EducationSome College                                                     *  
## EthnicityWhite                                                               
## IncomeLevel>$150k                                                         ***
## IncomeLevel$100-150k                                                      ***
## IncomeLevel$20-50k                                                        ***
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       ** 
## CESD.20.1                                                                    
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                 ** 
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                  ***
## RelationshipstatusWidowed                                                 ***
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         ***
## LivingstatusOther                                                         ***
## AnxietyYes                                                                   
## MoodDisordYes                                                             ** 
## Chronicconditions                                                         ***
## timefactor2:PandemicFU2 data collected before COVID-19                    *  
## timefactor3:PandemicFU2 data collected before COVID-19                    ** 
## timefactor2:Age_sexFemales 65+                                               
## timefactor3:Age_sexFemales 65+                                               
## timefactor2:Age_sexMales 45-64                                               
## timefactor3:Age_sexMales 45-64                                            .  
## timefactor2:Age_sexMales 65+                                                 
## timefactor3:Age_sexMales 65+                                                 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               .  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 .  
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 47 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

13.3.2) Estimated marginal means

Significant differences for males 65+

lsmeans(modelPASE_3, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  173.4374 4.876716 Inf 163.87919  182.9956
##  FU2 data collected before COVID-19 162.2234 4.740156 Inf 152.93285  171.5139
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  172.8390 5.648363 Inf 161.76845  183.9096
##  FU2 data collected before COVID-19 152.1409 5.383113 Inf 141.59019  162.6916
## 
## timefactor = 3, Age_sex = Females 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  147.0738 5.710253 Inf 135.88194  158.2657
##  FU2 data collected before COVID-19 148.6890 5.188934 Inf 138.51883  158.8591
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  127.6010 5.302595 Inf 117.20814  137.9939
##  FU2 data collected before COVID-19 121.2582 5.045488 Inf 111.36921  131.1472
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  116.9693 7.080697 Inf 103.09136  130.8472
##  FU2 data collected before COVID-19 114.6372 6.231594 Inf 102.42349  126.8509
## 
## timefactor = 3, Age_sex = Females 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  101.5476 7.197271 Inf  87.44116  115.6539
##  FU2 data collected before COVID-19 108.3568 5.930499 Inf  96.73323  119.9804
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  198.0288 4.715543 Inf 188.78647  207.2711
##  FU2 data collected before COVID-19 182.5684 4.859823 Inf 173.04331  192.0935
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  191.6727 5.408226 Inf 181.07278  202.2726
##  FU2 data collected before COVID-19 178.7899 5.961582 Inf 167.10538  190.4743
## 
## timefactor = 3, Age_sex = Males 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  180.6627 5.445629 Inf 169.98948  191.3359
##  FU2 data collected before COVID-19 168.4584 5.702579 Inf 157.28156  179.6353
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  132.2786 5.257342 Inf 121.97436  142.5828
##  FU2 data collected before COVID-19 129.1930 5.010991 Inf 119.37165  139.0144
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  136.2238 6.695998 Inf 123.09991  149.3477
##  FU2 data collected before COVID-19 118.7978 6.347852 Inf 106.35629  131.2394
## 
## timefactor = 3, Age_sex = Males 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  104.6550 7.176004 Inf  90.59029  118.7197
##  FU2 data collected before COVID-19 125.4085 6.017013 Inf 113.61532  137.2016
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelPASE_3, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   11.213990 2.607303 Inf   4.301  <.0001
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   20.698138 4.615920 Inf   4.484  <.0001
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   -1.615127 4.448075 Inf  -0.363  0.7165
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##    6.342850 3.678247 Inf   1.724  0.0846
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##    2.332081 6.948368 Inf   0.336  0.7371
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   -6.809245 6.916113 Inf  -0.985  0.3248
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   15.460376 2.635604 Inf   5.866  <.0001
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   12.882844 5.079743 Inf   2.536  0.0112
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   12.204310 4.784730 Inf   2.551  0.0108
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##    3.085549 3.521855 Inf   0.876  0.3810
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   17.425972 6.699385 Inf   2.601  0.0093
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##  -20.753451 6.891802 Inf  -3.011  0.0026
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelPASE_3, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##   11.213990 2.607303 Inf   6.10377 16.324209
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##   20.698138 4.615920 Inf  11.65110 29.745174
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##   -1.615127 4.448075 Inf -10.33319  7.102940
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##    6.342850 3.678247 Inf  -0.86638 13.552082
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##    2.332081 6.948368 Inf -11.28647 15.950631
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##   -6.809245 6.916113 Inf -20.36458  6.746087
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##   15.460376 2.635604 Inf  10.29469 20.626066
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##   12.882844 5.079743 Inf   2.92673 22.838958
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##   12.204310 4.784730 Inf   2.82641 21.582209
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##    3.085549 3.521855 Inf  -3.81716  9.988258
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##   17.425972 6.699385 Inf   4.29542 30.556526
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##  -20.753451 6.891802 Inf -34.26113 -7.245767
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

13.3.3) Graph of estimated marginal means

PASE_lsmeans_3 <- summary(lsmeans(modelPASE_3, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.

PASE_lsmeans_3$Time<-NA
PASE_lsmeans_3$Time[PASE_lsmeans_3$timefactor==1]<-"Baseline"
PASE_lsmeans_3$Time[PASE_lsmeans_3$timefactor==2]<-"Follow-up 1"
PASE_lsmeans_3$Time[PASE_lsmeans_3$timefactor==3]<-"Follow-up 2"
ggplot(PASE_lsmeans_3, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "MAT Score", title = "PASE Total Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

13.4) Full model adjusted for baseline (age and sex differences)

13.4.1) Model

modelPASE_4<- lmer(PASE_TOTAL ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= Tracking.data_long_2)
summary(modelPASE_4)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## PASE_TOTAL ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +  
##     IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +  
##     Livingstatus + Anxiety + MoodDisord + Chronicconditions +  
##     PASE_TOTALbaseline + (1 | ID)
##    Data: Tracking.data_long_2
## 
## REML criterion at convergence: 41443.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5900 -0.5394 -0.0518  0.4975  4.3962 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 1281     35.79   
##  Residual             2248     47.41   
## Number of obs: 3793, groups:  ID, 3084
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                 93.33664
## timefactor2                                                                -25.70659
## PandemicFU2 data collected before COVID-19                                 -13.80217
## Age_sexFemales 65+                                                         -35.79615
## Age_sexMales 45-64                                                          13.17052
## Age_sexMales 65+                                                           -17.99018
## EducationHigh School Diploma                                               -11.03172
## EducationLess than High School Diploma                                      -6.09233
## EducationSome College                                                        3.85447
## EthnicityWhite                                                              12.53759
## IncomeLevel>$150k                                                           11.95503
## IncomeLevel$100-150k                                                        15.37351
## IncomeLevel$20-50k                                                           5.44354
## IncomeLevel$50-100k                                                         10.93393
## BMI                                                                         -0.30116
## CESD.10baseline                                                              0.33349
## SmokingStatusFormer Smoker                                                  -4.52178
## SmokingStatusNever Smoked                                                   -0.97084
## SmokingStatusOccasional Smoker                                              -6.13003
## RelationshipstatusMarried                                                   -2.29172
## RelationshipstatusSeparated                                                  5.86833
## RelationshipstatusSingle                                                    -3.53788
## RelationshipstatusWidowed                                                   -8.94497
## LivingstatusAssisted Living                                                -21.55437
## LivingstatusHouse                                                           15.13757
## LivingstatusOther                                                           35.77027
## AnxietyYes                                                                  -0.18008
## MoodDisordYes                                                               -8.86110
## Chronicconditions                                                           -2.46867
## PASE_TOTALbaseline                                                           0.41899
## timefactor2:PandemicFU2 data collected before COVID-19                      21.17509
## timefactor2:Age_sexFemales 65+                                              12.89167
## timefactor2:Age_sexMales 45-64                                              12.00009
## timefactor2:Age_sexMales 65+                                                -5.32152
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               14.33558
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                6.24467
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 -1.79579
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  -13.94747
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -16.63398
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     15.90578
##                                                                           Std. Error
## (Intercept)                                                                 11.92745
## timefactor2                                                                  4.63003
## PandemicFU2 data collected before COVID-19                                   4.66193
## Age_sexFemales 65+                                                           6.86117
## Age_sexMales 45-64                                                           4.83736
## Age_sexMales 65+                                                             6.17899
## EducationHigh School Diploma                                                 3.44383
## EducationLess than High School Diploma                                       5.46166
## EducationSome College                                                        3.98186
## EthnicityWhite                                                               6.65003
## IncomeLevel>$150k                                                            5.86836
## IncomeLevel$100-150k                                                         4.74613
## IncomeLevel$20-50k                                                           3.40474
## IncomeLevel$50-100k                                                          3.57438
## BMI                                                                          0.24216
## CESD.10baseline                                                              0.26816
## SmokingStatusFormer Smoker                                                   4.94836
## SmokingStatusNever Smoked                                                    5.11375
## SmokingStatusOccasional Smoker                                               8.62339
## RelationshipstatusMarried                                                    3.74714
## RelationshipstatusSeparated                                                  7.39034
## RelationshipstatusSingle                                                     5.20164
## RelationshipstatusWidowed                                                    5.26013
## LivingstatusAssisted Living                                                 14.67116
## LivingstatusHouse                                                            3.41900
## LivingstatusOther                                                           13.94851
## AnxietyYes                                                                   4.61549
## MoodDisordYes                                                                3.26584
## Chronicconditions                                                            0.53330
## PASE_TOTALbaseline                                                           0.01477
## timefactor2:PandemicFU2 data collected before COVID-19                       5.90005
## timefactor2:Age_sexFemales 65+                                               8.85680
## timefactor2:Age_sexMales 45-64                                               6.27872
## timefactor2:Age_sexMales 65+                                                 8.33972
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                8.46202
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                6.89888
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  8.17141
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   10.99223
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    8.75135
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     10.76307
##                                                                                   df
## (Intercept)                                                               3120.20879
## timefactor2                                                               2051.15112
## PandemicFU2 data collected before COVID-19                                3722.23633
## Age_sexFemales 65+                                                        3752.97886
## Age_sexMales 45-64                                                        3751.25218
## Age_sexMales 65+                                                          3752.94868
## EducationHigh School Diploma                                              2985.93780
## EducationLess than High School Diploma                                    2964.93494
## EducationSome College                                                     2911.26795
## EthnicityWhite                                                            3160.35277
## IncomeLevel>$150k                                                         2926.59990
## IncomeLevel$100-150k                                                      2920.70539
## IncomeLevel$20-50k                                                        2851.09864
## IncomeLevel$50-100k                                                       2859.91393
## BMI                                                                       2968.34592
## CESD.10baseline                                                           2925.64447
## SmokingStatusFormer Smoker                                                2930.08505
## SmokingStatusNever Smoked                                                 2927.48640
## SmokingStatusOccasional Smoker                                            2840.80522
## RelationshipstatusMarried                                                 2974.45173
## RelationshipstatusSeparated                                               2844.75947
## RelationshipstatusSingle                                                  2874.59836
## RelationshipstatusWidowed                                                 2923.73677
## LivingstatusAssisted Living                                               3021.54585
## LivingstatusHouse                                                         2944.42575
## LivingstatusOther                                                         3077.51156
## AnxietyYes                                                                2951.85121
## MoodDisordYes                                                             2962.52608
## Chronicconditions                                                         2881.45429
## PASE_TOTALbaseline                                                        2928.31196
## timefactor2:PandemicFU2 data collected before COVID-19                    1987.84379
## timefactor2:Age_sexFemales 65+                                            2134.11859
## timefactor2:Age_sexMales 45-64                                            2182.80706
## timefactor2:Age_sexMales 65+                                              2278.93539
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             3737.95537
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             3725.37940
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               3741.78771
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2087.59997
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2109.76182
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   2269.82735
##                                                                           t value
## (Intercept)                                                                 7.825
## timefactor2                                                                -5.552
## PandemicFU2 data collected before COVID-19                                 -2.961
## Age_sexFemales 65+                                                         -5.217
## Age_sexMales 45-64                                                          2.723
## Age_sexMales 65+                                                           -2.912
## EducationHigh School Diploma                                               -3.203
## EducationLess than High School Diploma                                     -1.115
## EducationSome College                                                       0.968
## EthnicityWhite                                                              1.885
## IncomeLevel>$150k                                                           2.037
## IncomeLevel$100-150k                                                        3.239
## IncomeLevel$20-50k                                                          1.599
## IncomeLevel$50-100k                                                         3.059
## BMI                                                                        -1.244
## CESD.10baseline                                                             1.244
## SmokingStatusFormer Smoker                                                 -0.914
## SmokingStatusNever Smoked                                                  -0.190
## SmokingStatusOccasional Smoker                                             -0.711
## RelationshipstatusMarried                                                  -0.612
## RelationshipstatusSeparated                                                 0.794
## RelationshipstatusSingle                                                   -0.680
## RelationshipstatusWidowed                                                  -1.701
## LivingstatusAssisted Living                                                -1.469
## LivingstatusHouse                                                           4.427
## LivingstatusOther                                                           2.564
## AnxietyYes                                                                 -0.039
## MoodDisordYes                                                              -2.713
## Chronicconditions                                                          -4.629
## PASE_TOTALbaseline                                                         28.375
## timefactor2:PandemicFU2 data collected before COVID-19                      3.589
## timefactor2:Age_sexFemales 65+                                              1.456
## timefactor2:Age_sexMales 45-64                                              1.911
## timefactor2:Age_sexMales 65+                                               -0.638
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               1.694
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.905
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -0.220
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  -1.269
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -1.901
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.478
##                                                                           Pr(>|t|)
## (Intercept)                                                               6.88e-15
## timefactor2                                                               3.19e-08
## PandemicFU2 data collected before COVID-19                                 0.00309
## Age_sexFemales 65+                                                        1.91e-07
## Age_sexMales 45-64                                                         0.00651
## Age_sexMales 65+                                                           0.00362
## EducationHigh School Diploma                                               0.00137
## EducationLess than High School Diploma                                     0.26474
## EducationSome College                                                      0.33312
## EthnicityWhite                                                             0.05948
## IncomeLevel>$150k                                                          0.04172
## IncomeLevel$100-150k                                                       0.00121
## IncomeLevel$20-50k                                                         0.10997
## IncomeLevel$50-100k                                                        0.00224
## BMI                                                                        0.21373
## CESD.10baseline                                                            0.21374
## SmokingStatusFormer Smoker                                                 0.36090
## SmokingStatusNever Smoked                                                  0.84944
## SmokingStatusOccasional Smoker                                             0.47723
## RelationshipstatusMarried                                                  0.54085
## RelationshipstatusSeparated                                                0.42723
## RelationshipstatusSingle                                                   0.49647
## RelationshipstatusWidowed                                                  0.08914
## LivingstatusAssisted Living                                                0.14189
## LivingstatusHouse                                                         9.88e-06
## LivingstatusOther                                                          0.01038
## AnxietyYes                                                                 0.96888
## MoodDisordYes                                                              0.00670
## Chronicconditions                                                         3.84e-06
## PASE_TOTALbaseline                                                         < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                     0.00034
## timefactor2:Age_sexFemales 65+                                             0.14566
## timefactor2:Age_sexMales 45-64                                             0.05611
## timefactor2:Age_sexMales 65+                                               0.52348
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              0.09033
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              0.36543
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                0.82607
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  0.20464
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  0.05747
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    0.13960
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                ** 
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                        ** 
## Age_sexMales 65+                                                          ** 
## EducationHigh School Diploma                                              ** 
## EducationLess than High School Diploma                                       
## EducationSome College                                                        
## EthnicityWhite                                                            .  
## IncomeLevel>$150k                                                         *  
## IncomeLevel$100-150k                                                      ** 
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       ** 
## BMI                                                                          
## CESD.10baseline                                                              
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                 .  
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         ***
## LivingstatusOther                                                         *  
## AnxietyYes                                                                   
## MoodDisordYes                                                             ** 
## Chronicconditions                                                         ***
## PASE_TOTALbaseline                                                        ***
## timefactor2:PandemicFU2 data collected before COVID-19                    ***
## timefactor2:Age_sexFemales 65+                                               
## timefactor2:Age_sexMales 45-64                                            .  
## timefactor2:Age_sexMales 65+                                                 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             .  
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 .  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 40 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

13.4.2) Estimated marginal means

Significant differences for females 45-64 and 65+ and significant differences males 65+

lsmeans(modelPASE_4, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  164.3075 7.930214 Inf 148.76454  179.8504
##  FU2 data collected before COVID-19 150.5053 7.703752 Inf 135.40623  165.6044
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  138.6009 7.954807 Inf 123.00974  154.1920
##  FU2 data collected before COVID-19 145.9738 7.568732 Inf 131.13936  160.8082
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  128.5113 9.112584 Inf 110.65098  146.3716
##  FU2 data collected before COVID-19 129.0447 8.440193 Inf 112.50226  145.5872
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  115.6964 9.126499 Inf  97.80878  133.5840
##  FU2 data collected before COVID-19 123.4574 8.168835 Inf 107.44681  139.4681
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  177.4780 7.789159 Inf 162.21152  192.7445
##  FU2 data collected before COVID-19 169.9205 8.093574 Inf 154.05738  185.7836
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  163.7715 7.771707 Inf 148.53922  179.0037
##  FU2 data collected before COVID-19 160.7551 7.974000 Inf 145.12635  176.3839
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  146.3173 8.746042 Inf 129.17537  163.4592
##  FU2 data collected before COVID-19 130.7193 8.426516 Inf 114.20367  147.2350
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  115.2892 9.125577 Inf  97.40338  133.1750
##  FU2 data collected before COVID-19 136.7721 8.225615 Inf 120.65018  152.8940
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelPASE_4, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   13.802165 4.661931 Inf   2.961  0.0031
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   -7.372924 4.451193 Inf  -1.656  0.0976
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   -0.533419 7.070369 Inf  -0.075  0.9399
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   -7.761034 7.180605 Inf  -1.081  0.2798
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##    7.557492 5.099919 Inf   1.482  0.1384
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##    3.016380 4.797464 Inf   0.629  0.5295
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   15.597954 6.725774 Inf   2.319  0.0204
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##  -21.482911 6.934304 Inf  -3.098  0.0019
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelPASE_4, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##   13.802165 4.661931 Inf   4.66495 22.939382
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##   -7.372924 4.451193 Inf -16.09710  1.351255
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##   -0.533419 7.070369 Inf -14.39109 13.324250
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##   -7.761034 7.180605 Inf -21.83476  6.312694
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##    7.557492 5.099919 Inf  -2.43816 17.553149
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##    3.016380 4.797464 Inf  -6.38648 12.419236
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##   15.597954 6.725774 Inf   2.41568 28.780228
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df asymp.LCL asymp.UCL
##  -21.482911 6.934304 Inf -35.07390 -7.891924
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

13.4.3) Graph of estimated marginal means

PASE_lsmeans_4 <- summary(lsmeans(modelPASE_4, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.

PASE_lsmeans_4$Time<-NA
PASE_lsmeans_4$Time[PASE_lsmeans_4$timefactor==1]<-"Follow-up 1"
PASE_lsmeans_4$Time[PASE_lsmeans_4$timefactor==2]<-"Follow-up 2"
ggplot(PASE_lsmeans_4, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

14) Sedentary behaviour Results

14.1) Full model adjusted for baseline

Create binary variable for 4+ hrs/day of SB

Tracking.data_long_2$SB.binary <- as.factor(ifelse(Tracking.data_long_2$PASE_Sit==10, 1, 0))
Tracking.data_long_2$SBbaseline.binary <- as.factor(ifelse(Tracking.data_long_2$PASE_Sitbaseline==10, 1, 0))

14.1.1) Model

sit1 <- glmer(
  SB.binary ~ timefactor*Pandemic + Age + Sex + SBbaseline.binary + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = Tracking.data_long_2, 
  family = binomial(link = "logit")
)

## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations

## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 6.09947 (tol = 0.002, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

sit1

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: SB.binary ~ timefactor * Pandemic + Age + Sex + SBbaseline.binary +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: Tracking.data_long_2
##       AIC       BIC    logLik  deviance  df.resid 
##  25490.96  25736.57 -12714.48  25428.96     20362 
## Random effects:
##  Groups Name        Std.Dev.
##  ID     (Intercept) 0.8892  
## Number of obs: 20393, groups:  ID, 10230
## Fixed Effects:
##                                            (Intercept)  
##                                              -2.348903  
##                                            timefactor2  
##                                               0.481092  
##             PandemicFU2 data collected before COVID-19  
##                                               0.081017  
##                                                    Age  
##                                               0.015428  
##                                                   SexM  
##                                              -0.134353  
##                                     SBbaseline.binary1  
##                                               1.147014  
##                           EducationHigh School Diploma  
##                                              -0.004636  
##                 EducationLess than High School Diploma  
##                                               0.097418  
##                                  EducationSome College  
##                                              -0.083815  
##                                         EthnicityWhite  
##                                              -0.130210  
##                                      IncomeLevel>$150k  
##                                              -0.184708  
##                                   IncomeLevel$100-150k  
##                                              -0.011710  
##                                     IncomeLevel$20-50k  
##                                              -0.141811  
##                                    IncomeLevel$50-100k  
##                                              -0.218146  
##                                                    BMI  
##                                               0.039803  
##                                        CESD.10baseline  
##                                               0.012359  
##                             SmokingStatusFormer Smoker  
##                                              -0.300883  
##                              SmokingStatusNever Smoked  
##                                              -0.323008  
##                         SmokingStatusOccasional Smoker  
##                                              -0.338149  
##                              RelationshipstatusMarried  
##                                              -0.173398  
##                            RelationshipstatusSeparated  
##                                               0.098122  
##                               RelationshipstatusSingle  
##                                               0.067729  
##                              RelationshipstatusWidowed  
##                                               0.010810  
##                            LivingstatusAssisted Living  
##                                               0.296195  
##                                      LivingstatusHouse  
##                                              -0.232848  
##                                      LivingstatusOther  
##                                              -0.497680  
##                                             AnxietyYes  
##                                              -0.011266  
##                                          MoodDisordYes  
##                                               0.104958  
##                                      Chronicconditions  
##                                               0.037485  
## timefactor2:PandemicFU2 data collected before COVID-19  
##                                              -0.439137  
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 2 lme4 warnings

14.1.2) Predicted probabilities

ggpredict(sit1, c("timefactor", "Pandemic"))

## # Predicted probabilities of SB.binary
## 
## # Pandemic = FU2 data collected after COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.46 | [0.38, 0.54]
## 2          |      0.58 | [0.50, 0.65]
## 
## # Pandemic = FU2 data collected before COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.48 | [0.40, 0.56]
## 2          |      0.49 | [0.41, 0.57]
## 
## Adjusted for:
## *                Age =                    60.00
## *                Sex =                        F
## *  SBbaseline.binary =                        0
## *          Education = College Degree or Higher
## *          Ethnicity =                    Other
## *        IncomeLevel =                    <$20k
## *                BMI =                    27.52
## *    CESD.10baseline =                     4.87
## *      SmokingStatus =             Daily Smoker
## * Relationshipstatus =                 Divorced
## *       Livingstatus = Apartment/Condo/Townhome
## *            Anxiety =                       No
## *         MoodDisord =                       No
## *  Chronicconditions =                     2.76
## *                 ID =     0 (population-level)

14.1.3) Contrasts for predicted probilities

Mean Differences and 95% CIs

sit.test <- as.data.frame(ggpredict(sit1, c("timefactor", "Pandemic")))

mean.diff.1<-(subset(sit.test,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit.test,x==1 & group == "FU2 data collected before COVID-19")$predicted)
         
se.1<-(sqrt(((subset(sit.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
         (subset(sit.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))

UL.1<-mean.diff.1 + se.1*1.96
LL.1<-mean.diff.1 - se.1*1.96

         
mean.diff.2<-(subset(sit.test,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit.test,x==2 & group == "FU2 data collected before COVID-19")$predicted)
         
se.2<-(sqrt(((subset(sit.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
         (subset(sit.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))

UL.2<-mean.diff.2 + se.2*1.96
LL.2<-mean.diff.2 - se.2*1.96

#Mean differences and LL and UL
mean.diff.1

## [1] -0.02017452

UL.1

## [1] 0.2930919

LL.1

## [1] -0.3334409

mean.diff.2

## [1] 0.088871

UL.2

## [1] 0.4021841

LL.2

## [1] -0.2244421

z-scores

z.1<- (subset(sit.test,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit.test,x==1 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sit.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
     (subset(sit.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
     
z.2<- (subset(sit.test,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit.test,x==2 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sit.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
         (subset(sit.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))

z.1

## [1] -0.1262251

z.2

## [1] 0.5559523

p-values for z-scores

2*pnorm(z.1, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 1.100446

2*pnorm(z.2, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.5782435

14.1.4) Graph of predicted probabilities

ggpredict(sit1, c("timefactor", "Pandemic")) %>% plot()

14.2) Full model adjusted for baseline (age and sex differences)

14.2.1) Model

sit2 <- glmer(
  SB.binary ~ timefactor*Pandemic*Age_sex + SBbaseline.binary + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = Tracking.data_long_2, 
  family = binomial(link = "logit")
)

## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations

## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.884797 (tol = 0.002, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

sit2

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: SB.binary ~ timefactor * Pandemic * Age_sex + SBbaseline.binary +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: Tracking.data_long_2
##       AIC       BIC    logLik  deviance  df.resid 
##  25497.29  25822.13 -12707.64  25415.29     20352 
## Random effects:
##  Groups Name        Std.Dev.
##  ID     (Intercept) 0.901   
## Number of obs: 20393, groups:  ID, 10230
## Fixed Effects:
##                                                               (Intercept)  
##                                                                 -1.620686  
##                                                               timefactor2  
##                                                                  0.580652  
##                                PandemicFU2 data collected before COVID-19  
##                                                                  0.057124  
##                                                        Age_sexFemales 65+  
##                                                                  0.398934  
##                                                        Age_sexMales 45-64  
##                                                                 -0.146265  
##                                                          Age_sexMales 65+  
##                                                                  0.303371  
##                                                        SBbaseline.binary1  
##                                                                  1.150707  
##                                              EducationHigh School Diploma  
##                                                                 -0.005042  
##                                    EducationLess than High School Diploma  
##                                                                  0.096709  
##                                                     EducationSome College  
##                                                                 -0.073105  
##                                                            EthnicityWhite  
##                                                                 -0.054078  
##                                                         IncomeLevel>$150k  
##                                                                 -0.206336  
##                                                      IncomeLevel$100-150k  
##                                                                 -0.032560  
##                                                        IncomeLevel$20-50k  
##                                                                 -0.162937  
##                                                       IncomeLevel$50-100k  
##                                                                 -0.243375  
##                                                                       BMI  
##                                                                  0.039126  
##                                                           CESD.10baseline  
##                                                                  0.011728  
##                                                SmokingStatusFormer Smoker  
##                                                                 -0.290040  
##                                                 SmokingStatusNever Smoked  
##                                                                 -0.315386  
##                                            SmokingStatusOccasional Smoker  
##                                                                 -0.327601  
##                                                 RelationshipstatusMarried  
##                                                                 -0.176033  
##                                               RelationshipstatusSeparated  
##                                                                  0.061149  
##                                                  RelationshipstatusSingle  
##                                                                  0.068453  
##                                                 RelationshipstatusWidowed  
##                                                                  0.044016  
##                                               LivingstatusAssisted Living  
##                                                                  0.309546  
##                                                         LivingstatusHouse  
##                                                                 -0.239899  
##                                                         LivingstatusOther  
##                                                                 -0.451692  
##                                                                AnxietyYes  
##                                                                 -0.039052  
##                                                             MoodDisordYes  
##                                                                  0.112731  
##                                                         Chronicconditions  
##                                                                  0.042309  
##                    timefactor2:PandemicFU2 data collected before COVID-19  
##                                                                 -0.342007  
##                                            timefactor2:Age_sexFemales 65+  
##                                                                 -0.141938  
##                                            timefactor2:Age_sexMales 45-64  
##                                                                 -0.060952  
##                                              timefactor2:Age_sexMales 65+  
##                                                                 -0.124570  
##             PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  
##                                                                 -0.015392  
##             PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  
##                                                                  0.104367  
##               PandemicFU2 data collected before COVID-19:Age_sexMales 65+  
##                                                                  0.094448  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  
##                                                                 -0.173610  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  
##                                                                 -0.037797  
##   timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+  
##                                                                 -0.387304  
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 2 lme4 warnings

14.2.2) Predicted probabilities

ggpredict(sit2, c("Pandemic","timefactor","Age_sex"))

## # Predicted probabilities of SB.binary
## 
## # timefactor = 1
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.41 | [0.33, 0.49]
## FU2 data collected before COVID-19 |      0.42 | [0.35, 0.50]
## 
## # timefactor = 2
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.55 | [0.47, 0.63]
## FU2 data collected before COVID-19 |      0.48 | [0.40, 0.56]
## 
## # timefactor = 1
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.51 | [0.42, 0.60]
## FU2 data collected before COVID-19 |      0.52 | [0.43, 0.60]
## 
## # timefactor = 2
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.61 | [0.53, 0.70]
## FU2 data collected before COVID-19 |      0.50 | [0.41, 0.58]
## 
## # timefactor = 1
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.37 | [0.30, 0.45]
## FU2 data collected before COVID-19 |      0.41 | [0.33, 0.50]
## 
## # timefactor = 2
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.50 | [0.42, 0.58]
## FU2 data collected before COVID-19 |      0.45 | [0.36, 0.53]
## 
## # timefactor = 1
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.48 | [0.39, 0.57]
## FU2 data collected before COVID-19 |      0.52 | [0.44, 0.61]
## 
## # timefactor = 2
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.60 | [0.51, 0.68]
## FU2 data collected before COVID-19 |      0.45 | [0.37, 0.54]
## 
## Adjusted for:
## *  SBbaseline.binary =                        0
## *          Education = College Degree or Higher
## *          Ethnicity =                    Other
## *        IncomeLevel =                    <$20k
## *                BMI =                    27.52
## *    CESD.10baseline =                     4.87
## *      SmokingStatus =             Daily Smoker
## * Relationshipstatus =                 Divorced
## *       Livingstatus = Apartment/Condo/Townhome
## *            Anxiety =                       No
## *         MoodDisord =                       No
## *  Chronicconditions =                     2.76
## *                 ID =     0 (population-level)

14.2.3) Contrasts for predicted probilities

Mean differences and 95% CIs

sit.test2 <- as.data.frame(ggpredict(sit2, c("timefactor", "Pandemic", "Age_sex")))

#Females 45-64 years
mean.diff.Females.Young.1<-(subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
         
se.Females.Young.1<-(sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))

UL.Females.Young.1<-mean.diff.Females.Young.1 + se.Females.Young.1*1.96
LL.Females.Young.1<-mean.diff.Females.Young.1 - se.Females.Young.1*1.96

mean.diff.Females.Young.2<-(subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
         
se.Females.Young.2<-(sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
     (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))

UL.Females.Young.2<-mean.diff.Females.Young.2 + se.Females.Young.2*1.96
LL.Females.Young.2<-mean.diff.Females.Young.2 - se.Females.Young.2*1.96

#Females 65+ years
mean.diff.Females.Old.1<-(subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
         
se.Females.Old.1<-(sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))

UL.Females.Old.1<-mean.diff.Females.Old.1 + se.Females.Old.1*1.96
LL.Females.Old.1<-mean.diff.Females.Old.1 - se.Females.Old.1*1.96

mean.diff.Females.Old.2<-(subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
         
se.Females.Old.2<-(sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
     (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))

UL.Females.Old.2<-mean.diff.Females.Old.2 + se.Females.Old.2*1.96
LL.Females.Old.2<-mean.diff.Females.Old.2 - se.Females.Old.2*1.96

#Males 45-64 years
mean.diff.Males.Young.1<-(subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
         
se.Males.Young.1<-(sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))

UL.Males.Young.1<-mean.diff.Males.Young.1 + se.Males.Young.1*1.96
LL.Males.Young.1<-mean.diff.Males.Young.1 - se.Males.Young.1*1.96

mean.diff.Males.Young.2<-(subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
         
se.Males.Young.2<-(sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
     (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))

UL.Males.Young.2<-mean.diff.Males.Young.2 + se.Males.Young.2*1.96
LL.Males.Young.2<-mean.diff.Males.Young.2 - se.Males.Young.2*1.96

#Males 65+ years
mean.diff.Males.Old.1<-(subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
         
se.Males.Old.1<-(sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))

UL.Males.Old.1<-mean.diff.Males.Old.1 + se.Males.Old.1*1.96
LL.Males.Old.1<-mean.diff.Males.Old.1 - se.Males.Old.1*1.96

mean.diff.Males.Old.2<-(subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
         
se.Males.Old.2<-(sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
     (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))

UL.Males.Old.2<-mean.diff.Males.Old.2 + se.Males.Old.2*1.96
LL.Males.Old.2<-mean.diff.Males.Old.2 - se.Males.Old.2*1.96



#Mean differences and LL and UL
mean.diff.Females.Young.1

## [1] -0.01387204

UL.Females.Young.1

## [1] 0.3166355

LL.Females.Young.1

## [1] -0.3443796

mean.diff.Females.Young.2

## [1] 0.07101829

UL.Females.Young.2

## [1] 0.4004958

LL.Females.Young.2

## [1] -0.2584592

mean.diff.Females.Old.1

## [1] -0.01042611

UL.Females.Old.1

## [1] 0.3382217

LL.Females.Old.1

## [1] -0.3590739

mean.diff.Females.Old.2

## [1] 0.1163847

UL.Females.Old.2

## [1] 0.4654892

LL.Females.Old.2

## [1] -0.2327198

mean.diff.Males.Young.1

## [1] -0.03849999

UL.Males.Young.1

## [1] 0.2976257

LL.Males.Young.1

## [1] -0.3746256

mean.diff.Males.Young.2

## [1] 0.05437307

UL.Males.Young.2

## [1] 0.3894572

LL.Males.Young.2

## [1] -0.2807111

mean.diff.Males.Old.1

## [1] -0.0378739

UL.Males.Old.1

## [1] 0.3159752

LL.Males.Old.1

## [1] -0.391723

mean.diff.Males.Old.2

## [1] 0.1430834

UL.Males.Old.2

## [1] 0.4970302

LL.Males.Old.2

## [1] -0.2108635

z-scores

z.Females.Young.1 <- (subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
     
z.Females.Young.2 <- (subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
         (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))

z.Females.Old.1 <- (subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
     
z.Females.Old.2 <- (subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
         (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))

z.Males.Young.1 <- (subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
     
z.Males.Young.2 <- (subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
         (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))

z.Males.Old.1 <- (subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
     (subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
     
z.Males.Old.2 <- (subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
         (subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))

#z-scores
z.Females.Young.1

## [1] -0.08226499

z.Females.Young.2

## [1] 0.4224746

z.Males.Young.1

## [1] -0.2244993

z.Males.Young.2

## [1] 0.3180431

z.Females.Old.1

## [1] -0.05861263

z.Females.Old.2

## [1] 0.653426

z.Males.Old.1

## [1] -0.2097867

z.Males.Old.2

## [1] 0.792332

p-values for z-scores

2*pnorm(z.Females.Young.1, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 1.065564

2*pnorm(z.Females.Young.2, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.6726787

2*pnorm(z.Males.Young.1, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 1.177631

2*pnorm(z.Males.Young.2, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.7504522

2*pnorm(z.Females.Old.1, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 1.046739

2*pnorm(z.Females.Old.2, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.5134817

2*pnorm(z.Males.Old.1, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 1.166166

2*pnorm(z.Males.Old.2, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.4281671

14.2.4) Graph of predicted probabilities

ggpredict(sit2, c("timefactor","Pandemic","Age_sex")) %>% plot()

15) Sleep Results

15.1) Full model adjusted for baseline

15.1.1) Model

sleep1 <- glmer(
  RSTLS_Sleep ~ timefactor*Pandemic + Age + Sex + RSTLS_Sleepbaseline + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = Tracking.data_long_2, 
  family = binomial(link = "logit")
)

## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations

## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 1.36972 (tol = 0.002, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

sleep1

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## RSTLS_Sleep ~ timefactor * Pandemic + Age + Sex + RSTLS_Sleepbaseline +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: Tracking.data_long_2
##       AIC       BIC    logLik  deviance  df.resid 
##  24480.69  24726.86 -12209.35  24418.69     20731 
## Random effects:
##  Groups Name        Std.Dev.
##  ID     (Intercept) 0.8947  
## Number of obs: 20762, groups:  ID, 10407
## Fixed Effects:
##                                            (Intercept)  
##                                              -1.101766  
##                                            timefactor2  
##                                              -0.184424  
##             PandemicFU2 data collected before COVID-19  
##                                              -0.007853  
##                                                    Age  
##                                              -0.011876  
##                                                   SexM  
##                                              -0.209542  
##                                    RSTLS_Sleepbaseline  
##                                               0.913048  
##                           EducationHigh School Diploma  
##                                               0.139834  
##                 EducationLess than High School Diploma  
##                                               0.224461  
##                                  EducationSome College  
##                                               0.148781  
##                                         EthnicityWhite  
##                                               0.159890  
##                                      IncomeLevel>$150k  
##                                              -0.062121  
##                                   IncomeLevel$100-150k  
##                                               0.013985  
##                                     IncomeLevel$20-50k  
##                                              -0.044483  
##                                    IncomeLevel$50-100k  
##                                               0.009227  
##                                                    BMI  
##                                               0.001927  
##                                        CESD.10baseline  
##                                               0.063722  
##                             SmokingStatusFormer Smoker  
##                                               0.051360  
##                              SmokingStatusNever Smoked  
##                                              -0.066645  
##                         SmokingStatusOccasional Smoker  
##                                              -0.105624  
##                              RelationshipstatusMarried  
##                                              -0.059996  
##                            RelationshipstatusSeparated  
##                                              -0.383550  
##                               RelationshipstatusSingle  
##                                              -0.216066  
##                              RelationshipstatusWidowed  
##                                              -0.147863  
##                            LivingstatusAssisted Living  
##                                               0.206873  
##                                      LivingstatusHouse  
##                                               0.140978  
##                                      LivingstatusOther  
##                                              -0.055422  
##                                             AnxietyYes  
##                                              -0.145088  
##                                          MoodDisordYes  
##                                              -0.032438  
##                                      Chronicconditions  
##                                               0.086628  
## timefactor2:PandemicFU2 data collected before COVID-19  
##                                              -0.011079  
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 2 lme4 warnings

15.1.2) Predicted probabilities

ggpredict(sleep1, c("timefactor", "Pandemic"))

## # Predicted probabilities of RSTLS_Sleep
## 
## # Pandemic = FU2 data collected after COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.29 | [0.22, 0.36]
## 2          |      0.25 | [0.19, 0.32]
## 
## # Pandemic = FU2 data collected before COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.28 | [0.22, 0.35]
## 2          |      0.25 | [0.19, 0.31]
## 
## Adjusted for:
## *                 Age =                    60.00
## *                 Sex =                        F
## * RSTLS_Sleepbaseline =                     0.33
## *           Education = College Degree or Higher
## *           Ethnicity =                    Other
## *         IncomeLevel =                    <$20k
## *                 BMI =                    27.53
## *     CESD.10baseline =                     4.88
## *       SmokingStatus =             Daily Smoker
## *  Relationshipstatus =                 Divorced
## *        Livingstatus = Apartment/Condo/Townhome
## *             Anxiety =                       No
## *          MoodDisord =                       No
## *   Chronicconditions =                     2.76
## *                  ID =     0 (population-level)

15.1.3) Contrasts for predicted probilities

Mean Differences and 95% CIs

sleep.test <- as.data.frame(ggpredict(sleep1, c("timefactor", "Pandemic")))

mean.diff.3<-(subset(sleep.test,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep.test,x==1 & group == "FU2 data collected before COVID-19")$predicted)
         
se.3<-(sqrt(((subset(sleep.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
         (subset(sleep.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))

UL.3<-mean.diff.3 + se.3*1.96
LL.3<-mean.diff.3 - se.3*1.96

         
mean.diff.4<-(subset(sleep.test,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep.test,x==2 & group == "FU2 data collected before COVID-19")$predicted)
         
se.4<-(sqrt(((subset(sleep.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
         (subset(sleep.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))

UL.4<-mean.diff.4 + se.4*1.96
LL.4<-mean.diff.4 - se.4*1.96

#Mean differences and LL and UL
mean.diff.3

## [1] 0.001602157

UL.3

## [1] 0.3240006

LL.3

## [1] -0.3207962

mean.diff.4

## [1] 0.003534739

UL.4

## [1] 0.3265404

LL.4

## [1] -0.319471

z-scores

z.3<- (subset(sleep.test,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep.test,x==1 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sleep.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
                                                                                                     (subset(sleep.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))

z.4<- (subset(sleep.test,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep.test,x==2 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sleep.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
                                                                                                     (subset(sleep.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))

#z-scores
z.3

## [1] 0.009740209

z.4

## [1] 0.02144881

p-values for z-scores

2*pnorm(z.3, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.9922286

2*pnorm(z.4, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.9828876

15.1.4) Graph of predicted probabilities

ggpredict(sleep1, c("timefactor", "Pandemic")) %>% plot()

15.2) Full model adjusted for baseline (age and sex differences)

15.2.1) Model

sleep2 <- glmer(
  RSTLS_Sleep ~ timefactor*Pandemic*Age_sex + RSTLS_Sleepbaseline + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = Tracking.data_long_2, 
  family = binomial(link = "logit")
)

## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations

## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 3.59238 (tol = 0.002, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

sleep2

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: RSTLS_Sleep ~ timefactor * Pandemic * Age_sex + RSTLS_Sleepbaseline +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: Tracking.data_long_2
##       AIC       BIC    logLik  deviance  df.resid 
##  24481.72  24807.30 -12199.86  24399.72     20721 
## Random effects:
##  Groups Name        Std.Dev.
##  ID     (Intercept) 0.8988  
## Number of obs: 20762, groups:  ID, 10407
## Fixed Effects:
##                                                               (Intercept)  
##                                                                 -1.671773  
##                                                               timefactor2  
##                                                                 -0.281688  
##                                PandemicFU2 data collected before COVID-19  
##                                                                 -0.155181  
##                                                        Age_sexFemales 65+  
##                                                                 -0.226663  
##                                                        Age_sexMales 45-64  
##                                                                 -0.510486  
##                                                          Age_sexMales 65+  
##                                                                 -0.477572  
##                                                       RSTLS_Sleepbaseline  
##                                                                  0.911139  
##                                              EducationHigh School Diploma  
##                                                                  0.135689  
##                                    EducationLess than High School Diploma  
##                                                                  0.204534  
##                                                     EducationSome College  
##                                                                  0.132670  
##                                                            EthnicityWhite  
##                                                                  0.131442  
##                                                         IncomeLevel>$150k  
##                                                                  0.014420  
##                                                      IncomeLevel$100-150k  
##                                                                  0.077726  
##                                                        IncomeLevel$20-50k  
##                                                                 -0.022403  
##                                                       IncomeLevel$50-100k  
##                                                                  0.037373  
##                                                                       BMI  
##                                                                  0.003336  
##                                                           CESD.10baseline  
##                                                                  0.064369  
##                                                SmokingStatusFormer Smoker  
##                                                                  0.031118  
##                                                 SmokingStatusNever Smoked  
##                                                                 -0.077333  
##                                            SmokingStatusOccasional Smoker  
##                                                                 -0.088391  
##                                                 RelationshipstatusMarried  
##                                                                 -0.053105  
##                                               RelationshipstatusSeparated  
##                                                                 -0.359240  
##                                                  RelationshipstatusSingle  
##                                                                 -0.191453  
##                                                 RelationshipstatusWidowed  
##                                                                 -0.167265  
##                                               LivingstatusAssisted Living  
##                                                                  0.229839  
##                                                         LivingstatusHouse  
##                                                                  0.165971  
##                                                         LivingstatusOther  
##                                                                 -0.064622  
##                                                                AnxietyYes  
##                                                                 -0.121646  
##                                                             MoodDisordYes  
##                                                                 -0.026124  
##                                                         Chronicconditions  
##                                                                  0.084981  
##                    timefactor2:PandemicFU2 data collected before COVID-19  
##                                                                  0.107955  
##                                            timefactor2:Age_sexFemales 65+  
##                                                                  0.103330  
##                                            timefactor2:Age_sexMales 45-64  
##                                                                  0.256567  
##                                              timefactor2:Age_sexMales 65+  
##                                                                 -0.159097  
##             PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  
##                                                                 -0.076275  
##             PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  
##                                                                  0.365992  
##               PandemicFU2 data collected before COVID-19:Age_sexMales 65+  
##                                                                  0.140755  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  
##                                                                 -0.134282  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  
##                                                                 -0.374952  
##   timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+  
##                                                                  0.245235  
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 2 lme4 warnings

15.2.2) Predicted probabilities

ggpredict(sleep2, c("Pandemic","timefactor","Age_sex"))

## # Predicted probabilities of RSTLS_Sleep
## 
## # timefactor = 1
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.32 | [0.25, 0.40]
## FU2 data collected before COVID-19 |      0.29 | [0.23, 0.36]
## 
## # timefactor = 2
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.27 | [0.20, 0.34]
## FU2 data collected before COVID-19 |      0.26 | [0.20, 0.33]
## 
## # timefactor = 1
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.28 | [0.21, 0.36]
## FU2 data collected before COVID-19 |      0.23 | [0.18, 0.30]
## 
## # timefactor = 2
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.24 | [0.18, 0.32]
## FU2 data collected before COVID-19 |      0.20 | [0.15, 0.26]
## 
## # timefactor = 1
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.22 | [0.17, 0.29]
## FU2 data collected before COVID-19 |      0.26 | [0.20, 0.33]
## 
## # timefactor = 2
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.22 | [0.17, 0.28]
## FU2 data collected before COVID-19 |      0.21 | [0.16, 0.27]
## 
## # timefactor = 1
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.23 | [0.17, 0.30]
## FU2 data collected before COVID-19 |      0.23 | [0.17, 0.30]
## 
## # timefactor = 2
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.16 | [0.12, 0.22]
## FU2 data collected before COVID-19 |      0.21 | [0.16, 0.28]
## 
## Adjusted for:
## * RSTLS_Sleepbaseline =                     0.33
## *           Education = College Degree or Higher
## *           Ethnicity =                    Other
## *         IncomeLevel =                    <$20k
## *                 BMI =                    27.53
## *     CESD.10baseline =                     4.88
## *       SmokingStatus =             Daily Smoker
## *  Relationshipstatus =                 Divorced
## *        Livingstatus = Apartment/Condo/Townhome
## *             Anxiety =                       No
## *          MoodDisord =                       No
## *   Chronicconditions =                     2.76
## *                  ID =     0 (population-level)

15.2.3) Contrasts for predicted probilities

Mean differences and 95% CIs

sleep.test2 <- as.data.frame(ggpredict(sleep2, c("timefactor", "Pandemic", "Age_sex")))

#Females 45-64 years
mean.diff.Females.Young.3<-(subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
         
se.Females.Young.3<-(sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
     (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))

UL.Females.Young.3<-mean.diff.Females.Young.3 + se.Females.Young.3*1.96
LL.Females.Young.3<-mean.diff.Females.Young.3 - se.Females.Young.3*1.96

mean.diff.Females.Young.4<-(subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
         
se.Females.Young.4<-(sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
     (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))

UL.Females.Young.4<-mean.diff.Females.Young.4 + se.Females.Young.4*1.96
LL.Females.Young.4<-mean.diff.Females.Young.4 - se.Females.Young.4*1.96

#Females 65+ years
mean.diff.Females.Old.3<-(subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
         
se.Females.Old.3<-(sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
     (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))

UL.Females.Old.3<-mean.diff.Females.Old.3 + se.Females.Old.3*1.96
LL.Females.Old.3<-mean.diff.Females.Old.3 - se.Females.Old.3*1.96

mean.diff.Females.Old.4<-(subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
         
se.Females.Old.4<-(sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
     (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))

UL.Females.Old.4<-mean.diff.Females.Old.4 + se.Females.Old.4*1.96
LL.Females.Old.4<-mean.diff.Females.Old.4 - se.Females.Old.4*1.96

#Males 45-64 years
mean.diff.Males.Young.3<-(subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
         
se.Males.Young.3<-(sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
     (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))

UL.Males.Young.3<-mean.diff.Males.Young.3 + se.Males.Young.3*1.96
LL.Males.Young.3<-mean.diff.Males.Young.3 - se.Males.Young.3*1.96

mean.diff.Males.Young.4<-(subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
         
se.Males.Young.4<-(sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
     (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))

UL.Males.Young.4<-mean.diff.Males.Young.4 + se.Males.Young.4*1.96
LL.Males.Young.4<-mean.diff.Males.Young.4 - se.Males.Young.4*1.96

#Males 65+ years
mean.diff.Males.Old.3<-(subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
         
se.Males.Old.3<-(sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
     (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))

UL.Males.Old.3<-mean.diff.Males.Old.3 + se.Males.Old.3*1.96
LL.Males.Old.3<-mean.diff.Males.Old.3 - se.Males.Old.3*1.96

mean.diff.Males.Old.4<-(subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
         
se.Males.Old.4<-(sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
     (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))

UL.Males.Old.4<-mean.diff.Males.Old.4 + se.Males.Old.4*1.96
LL.Males.Old.4<-mean.diff.Males.Old.4 - se.Males.Old.4*1.96



#Mean differences and LL and UL
mean.diff.Females.Young.3

## [1] 0.0330407

UL.Females.Young.3

## [1] 0.3693358

LL.Females.Young.3

## [1] -0.3032544

mean.diff.Females.Young.4

## [1] 0.009115943

UL.Females.Young.4

## [1] 0.3465412

LL.Females.Young.4

## [1] -0.3283094

mean.diff.Females.Old.3

## [1] 0.04386866

UL.Females.Old.3

## [1] 0.4009714

LL.Females.Old.3

## [1] -0.3132341

mean.diff.Females.Old.4

## [1] 0.04418037

UL.Females.Old.4

## [1] 0.4030128

LL.Females.Old.4

## [1] -0.314652

mean.diff.Males.Young.3

## [1] -0.03871951

UL.Males.Young.3

## [1] 0.3049796

LL.Males.Young.3

## [1] -0.3824186

mean.diff.Males.Young.4

## [1] 0.009469295

UL.Males.Young.4

## [1] 0.3545126

LL.Males.Young.4

## [1] -0.335574

mean.diff.Males.Old.3

## [1] 0.002540603

UL.Males.Old.3

## [1] 0.3662314

LL.Males.Old.3

## [1] -0.3611502

mean.diff.Males.Old.4

## [1] -0.05110064

UL.Males.Old.4

## [1] 0.3168039

LL.Males.Old.4

## [1] -0.4190052

Z-scores

z.Females.Young.3 <- (subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
                        subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
                                                                                                                                                (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))

z.Females.Young.4 <- (subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
                        subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
                                                                                                                                                (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))

z.Females.Old.3 <- (subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
                      subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
                                                                                                                                            (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))

z.Females.Old.4 <- (subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
                      subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
                                                                                                                                            (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))

z.Males.Young.3 <- (subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
                      subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
                                                                                                                                            (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))

z.Males.Young.4 <- (subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
                      subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
                                                                                                                                            (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))

z.Males.Old.3 <- (subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
                    subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
                                                                                                                                        (subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))

z.Males.Old.4 <- (subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
                    subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
                                                                                                                                        (subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))

#z-scores
z.Females.Young.3

## [1] 0.1925683

z.Females.Young.4

## [1] 0.05295172

z.Males.Young.3

## [1] -0.2208043

z.Males.Young.4

## [1] 0.05378982

z.Females.Old.3

## [1] 0.2407782

z.Females.Old.4

## [1] 0.2413202

z.Males.Old.3

## [1] 0.0136918

z.Males.Old.4

## [1] -0.2722371

p-values for z-scores

2*pnorm(z.Females.Young.3, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.8472971

2*pnorm(z.Females.Young.4, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.9577704

2*pnorm(z.Males.Young.3, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 1.174755

2*pnorm(z.Males.Young.4, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.9571026

2*pnorm(z.Females.Old.3, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.809727

2*pnorm(z.Females.Old.4, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.8093069

2*pnorm(z.Males.Old.3, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.9890759

2*pnorm(z.Males.Old.4, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 1.21456

15.2.4) Graph of predicted probabilities

ggpredict(sleep2, c("timefactor","Pandemic","Age_sex")) %>% plot()

16) Sensitivity Analyses

16.1) Truncated Sample Development

For this sensitivity analysis, we only include participants w/ data collected in 2019 onward (N=16,649)

startdate <- as.POSIXct("2019-01-01 00:00:00", tz = "EST")
truncated <- subset(Tracking.Adjusted_Final, timestamp>startdate)

Linear mixed model set-up

truncated.short<-truncated[c(1,4:12,14,15,18:21,146,147,98,115,132,102,119,136,106,123,140,110,127,144,13,44,75,22,53,84,23,54,85)]

truncated.short2<-rename(truncated.short, c("Age"="Age_0","Sex"="Sex_0","Ethnicity"="Ethnicity_0","Relationshipstatus"="Relationship_status_0",
                             "Education"="Education4_0", "IncomeLevel"="Income_Level_0", "Livingstatus"="Living_status_0", 
                             "Alcohol"="Alcohol_0", "SmokingStatus"="Smoking_Status_0","Anxiety"="Anxiety_0","MoodDisord"="Mood_Disord_0",
                             "Chronicconditions"="Chronic_conditions_0", "BMI"="BMI_0","PASE_Sit_0"="PASE_Q1B_0","PASE_Sit_1"="PASE_Q1B_1","PASE_Sit_2"="PASE_Q1B_2"))

truncated.short2$PASE_TOTALbaseline <- truncated.short2$PASE_TOTAL_0

truncated.short3<-truncated.short2[c(1:18,40,19:39)]

truncated.short4<-truncated.short3[c(1:18,20,23,26,29,32,35,38,21:22,24:25,27:28,30:31,33:34,36:37,39:40)]

colnames(truncated.short3) <- (gsub("_2",".3",colnames(truncated.short3)))
colnames(truncated.short3) <- (gsub("_1",".2",colnames(truncated.short3)))
colnames(truncated.short3) <- (gsub("_0",".1",colnames(truncated.short3)))

colnames(truncated.short4) <- (gsub("_2",".2",colnames(truncated.short4)))
colnames(truncated.short4) <- (gsub("_1",".1",colnames(truncated.short4)))
colnames(truncated.short4) <- (gsub("_0","baseline",colnames(truncated.short4)))


truncated.data_long <- reshape(as.data.frame(truncated.short3),idvar="ID",varying=20:40,direction="long",sep=".") #reshape data into long format (3 timepoints)
truncated.data_long_2 <- reshape(as.data.frame(truncated.short4),idvar="ID",varying=26:39,direction="long",sep=".") #reshape data into long format (3 timepoints)

Indexed time as a categorical factor

#Treat time as a fixed effect
truncated.data_long$timefactor<-as.factor(truncated.data_long$time)
truncated.data_long_2$timefactor<-as.factor(truncated.data_long_2$time)

Age and Sex grouping

truncated.data_long$Age_sex<-NA
truncated.data_long$Age_sex[truncated.data_long$Age<=64 & truncated.data_long$Sex == "M"]<-"Males 45-64"
truncated.data_long$Age_sex[truncated.data_long$Age<=64 & truncated.data_long$Sex == "F"]<-"Females 45-64"
truncated.data_long$Age_sex[truncated.data_long$Age>64 & truncated.data_long$Sex == "M"]<-"Males 65+"
truncated.data_long$Age_sex[truncated.data_long$Age>64 & truncated.data_long$Sex == "F"]<-"Females 65+"

truncated.data_long_2$Age_sex<-NA
truncated.data_long_2$Age_sex[truncated.data_long_2$Age<=64 & truncated.data_long_2$Sex == "M"]<-"Males 45-64"
truncated.data_long_2$Age_sex[truncated.data_long_2$Age<=64 & truncated.data_long_2$Sex == "F"]<-"Females 45-64"
truncated.data_long_2$Age_sex[truncated.data_long_2$Age>64 & truncated.data_long_2$Sex == "M"]<-"Males 65+"
truncated.data_long_2$Age_sex[truncated.data_long_2$Age>64 & truncated.data_long_2$Sex == "F"]<-"Females 65+"

16.2) Truncated Sample Characteristics

Truncated full sample (N= 16,649)

Baseline<-dput(names(truncated[c(5,4,14,12,6,7,8,9,10,11,15,18,19,20,13,28,30,26,23)]))

## c("Age_0", "Sex_0", "BMI_0", "Ethnicity_0", "Relationship_status_0", 
## "Education4_0", "Income_Level_0", "Living_status_0", "Alcohol_0", 
## "Smoking_Status_0", "CESD_10_0", "Anxiety_0", "Mood_Disord_0", 
## "Pet_Owner_0", "PASE_TOTAL_0", "MAT_Score_0", "RVLT_Immediate_Score_0", 
## "Animal_Fluency_Lenient_0", "RSTLS_Sleep_0")

Table1_truncated<-CreateTableOne(vars=Baseline, data=truncated)
print(Table1_truncated,contDigits=2,missing=TRUE,quote=TRUE)

##                                               ""
##  ""                                            "Overall"        "Missing"
##   "n"                                          "  9423"         "    "   
##   "Age_0 (mean (SD))"                          " 61.17 (10.22)" " 0.0"   
##   "Sex_0 = M (%)"                              "  4624 (49.1) " " 0.0"   
##   "BMI_0 (mean (SD))"                          " 27.48 (5.05)"  " 0.5"   
##   "Ethnicity_0 = White (%)"                    "  9165 (97.3) " " 0.0"   
##   "Relationship_status_0 (%)"                  "  "             " 0.0"   
##   "   Divorced"                                "   813 ( 8.6) " "    "   
##   "   Married"                                 "  6900 (73.3) " "    "   
##   "   Separated"                               "   246 ( 2.6) " "    "   
##   "   Single"                                  "   694 ( 7.4) " "    "   
##   "   Widowed"                                 "   766 ( 8.1) " "    "   
##   "Education4_0 (%)"                           "  "             " 0.0"   
##   "   College Degree or Higher"                "  6879 (73.0) " "    "   
##   "   High School Diploma"                     "  1213 (12.9) " "    "   
##   "   Less than High School Diploma"           "   626 ( 6.6) " "    "   
##   "   Some College"                            "   705 ( 7.5) " "    "   
##   "Income_Level_0 (%)"                         "  "             " 3.6"   
##   "   <$20k"                                   "  1449 (16.0) " "    "   
##   "   >$150k"                                  "   380 ( 4.2) " "    "   
##   "   $100-150k"                               "   715 ( 7.9) " "    "   
##   "   $20-50k"                                 "  3534 (38.9) " "    "   
##   "   $50-100k"                                "  3004 (33.1) " "    "   
##   "Living_status_0 (%)"                        "  "             " 0.0"   
##   "   Apartment/Condo/Townhome"                "  1063 (11.3) " "    "   
##   "   Assisted Living"                         "    48 ( 0.5) " "    "   
##   "   House"                                   "  8233 (87.4) " "    "   
##   "   Other"                                   "    79 ( 0.8) " "    "   
##   "Alcohol_0 (%)"                              "  "             " 3.0"   
##   "   Non-drinker"                             "   929 (10.2) " "    "   
##   "   Occasional drinker"                      "  1439 (15.7) " "    "   
##   "   Regular drinker (at least once a month)" "  6773 (74.1) " "    "   
##   "Smoking_Status_0 (%)"                       "  "             " 0.5"   
##   "   Daily Smoker"                            "   628 ( 6.7) " "    "   
##   "   Former Smoker"                           "  5667 (60.4) " "    "   
##   "   Never Smoked"                            "  2927 (31.2) " "    "   
##   "   Occasional Smoker"                       "   156 ( 1.7) " "    "   
##   "CESD_10_0 (mean (SD))"                      "  4.97 (4.37)"  " 0.3"   
##   "Anxiety_0 = Yes (%)"                        "   598 ( 6.4) " " 0.1"   
##   "Mood_Disord_0 = Yes (%)"                    "  1266 (13.4) " " 0.1"   
##   "Pet_Owner_0 = Yes (%)"                      "  4526 (48.2) " " 0.4"   
##   "PASE_TOTAL_0 (mean (SD))"                   "172.78 (79.33)" "19.2"   
##   "MAT_Score_0 (mean (SD))"                    " 27.23 (9.14)"  " 0.0"   
##   "RVLT_Immediate_Score_0 (mean (SD))"         "  6.15 (2.25)"  " 0.0"   
##   "Animal_Fluency_Lenient_0 (mean (SD))"       " 22.08 (6.38)"  " 0.0"   
##   "RSTLS_Sleep_0 (mean (SD))"                  "  0.33 (0.47)"  " 0.2"

Final baseline sample stratified by whether FU2 data was collected before (N= 7132) or after (N= 6898) the start of the COVID-19 pandemic

Table1_truncated_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=truncated)
print(Table1_truncated_stratify,contDigits=2,missing=TRUE,quote=TRUE)

##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected after COVID-19"
##   "n"                                          "  5181"                           
##   "Age_0 (mean (SD))"                          " 60.29 (10.54)"                   
##   "Sex_0 = M (%)"                              "  2856 (55.1) "                   
##   "BMI_0 (mean (SD))"                          " 27.52 (4.94)"                    
##   "Ethnicity_0 = White (%)"                    "  5025 (97.0) "                   
##   "Relationship_status_0 (%)"                  "  "                               
##   "   Divorced"                                "   408 ( 7.9) "                   
##   "   Married"                                 "  3841 (74.2) "                   
##   "   Separated"                               "   150 ( 2.9) "                   
##   "   Single"                                  "   383 ( 7.4) "                   
##   "   Widowed"                                 "   397 ( 7.7) "                   
##   "Education4_0 (%)"                           "  "                               
##   "   College Degree or Higher"                "  3605 (69.6) "                   
##   "   High School Diploma"                     "   776 (15.0) "                   
##   "   Less than High School Diploma"           "   411 ( 7.9) "                   
##   "   Some College"                            "   389 ( 7.5) "                   
##   "Income_Level_0 (%)"                         "  "                               
##   "   <$20k"                                   "   783 (15.6) "                   
##   "   >$150k"                                  "   239 ( 4.8) "                   
##   "   $100-150k"                               "   426 ( 8.5) "                   
##   "   $20-50k"                                 "  1874 (37.3) "                   
##   "   $50-100k"                                "  1700 (33.9) "                   
##   "Living_status_0 (%)"                        "  "                               
##   "   Apartment/Condo/Townhome"                "   579 (11.2) "                   
##   "   Assisted Living"                         "    24 ( 0.5) "                   
##   "   House"                                   "  4541 (87.6) "                   
##   "   Other"                                   "    37 ( 0.7) "                   
##   "Alcohol_0 (%)"                              "  "                               
##   "   Non-drinker"                             "   512 (10.2) "                   
##   "   Occasional drinker"                      "   784 (15.6) "                   
##   "   Regular drinker (at least once a month)" "  3731 (74.2) "                   
##   "Smoking_Status_0 (%)"                       "  "                               
##   "   Daily Smoker"                            "   361 ( 7.0) "                   
##   "   Former Smoker"                           "  3128 (60.6) "                   
##   "   Never Smoked"                            "  1579 (30.6) "                   
##   "   Occasional Smoker"                       "    90 ( 1.7) "                   
##   "CESD_10_0 (mean (SD))"                      "  5.08 (4.47)"                    
##   "Anxiety_0 = Yes (%)"                        "   335 ( 6.5) "                   
##   "Mood_Disord_0 = Yes (%)"                    "   698 (13.5) "                   
##   "Pet_Owner_0 = Yes (%)"                      "  2562 (49.6) "                   
##   "PASE_TOTAL_0 (mean (SD))"                   "179.56 (81.39)"                   
##   "MAT_Score_0 (mean (SD))"                    " 27.11 (9.20)"                    
##   "RVLT_Immediate_Score_0 (mean (SD))"         "  5.98 (2.20)"                    
##   "Animal_Fluency_Lenient_0 (mean (SD))"       " 21.92 (6.41)"                    
##   "RSTLS_Sleep_0 (mean (SD))"                  "  0.33 (0.47)"                    
##                                               "Stratified by Pandemic"
##  ""                                            "FU2 data collected before COVID-19"
##   "n"                                          "  4242"                            
##   "Age_0 (mean (SD))"                          " 62.24 (9.72)"                     
##   "Sex_0 = M (%)"                              "  1768 (41.7) "                    
##   "BMI_0 (mean (SD))"                          " 27.43 (5.19)"                     
##   "Ethnicity_0 = White (%)"                    "  4140 (97.6) "                    
##   "Relationship_status_0 (%)"                  "  "                                
##   "   Divorced"                                "   405 ( 9.6) "                    
##   "   Married"                                 "  3059 (72.1) "                    
##   "   Separated"                               "    96 ( 2.3) "                    
##   "   Single"                                  "   311 ( 7.3) "                    
##   "   Widowed"                                 "   369 ( 8.7) "                    
##   "Education4_0 (%)"                           "  "                                
##   "   College Degree or Higher"                "  3274 (77.2) "                    
##   "   High School Diploma"                     "   437 (10.3) "                    
##   "   Less than High School Diploma"           "   215 ( 5.1) "                    
##   "   Some College"                            "   316 ( 7.4) "                    
##   "Income_Level_0 (%)"                         "  "                                
##   "   <$20k"                                   "   666 (16.4) "                    
##   "   >$150k"                                  "   141 ( 3.5) "                    
##   "   $100-150k"                               "   289 ( 7.1) "                    
##   "   $20-50k"                                 "  1660 (40.9) "                    
##   "   $50-100k"                                "  1304 (32.1) "                    
##   "Living_status_0 (%)"                        "  "                                
##   "   Apartment/Condo/Townhome"                "   484 (11.4) "                    
##   "   Assisted Living"                         "    24 ( 0.6) "                    
##   "   House"                                   "  3692 (87.0) "                    
##   "   Other"                                   "    42 ( 1.0) "                    
##   "Alcohol_0 (%)"                              "  "                                
##   "   Non-drinker"                             "   417 (10.1) "                    
##   "   Occasional drinker"                      "   655 (15.9) "                    
##   "   Regular drinker (at least once a month)" "  3042 (73.9) "                    
##   "Smoking_Status_0 (%)"                       "  "                                
##   "   Daily Smoker"                            "   267 ( 6.3) "                    
##   "   Former Smoker"                           "  2539 (60.2) "                    
##   "   Never Smoked"                            "  1348 (31.9) "                    
##   "   Occasional Smoker"                       "    66 ( 1.6) "                    
##   "CESD_10_0 (mean (SD))"                      "  4.83 (4.24)"                     
##   "Anxiety_0 = Yes (%)"                        "   263 ( 6.2) "                    
##   "Mood_Disord_0 = Yes (%)"                    "   568 (13.4) "                    
##   "Pet_Owner_0 = Yes (%)"                      "  1964 (46.5) "                    
##   "PASE_TOTAL_0 (mean (SD))"                   "164.63 (76.01)"                    
##   "MAT_Score_0 (mean (SD))"                    " 27.37 (9.06)"                     
##   "RVLT_Immediate_Score_0 (mean (SD))"         "  6.35 (2.28)"                     
##   "Animal_Fluency_Lenient_0 (mean (SD))"       " 22.28 (6.34)"                     
##   "RSTLS_Sleep_0 (mean (SD))"                  "  0.33 (0.47)"                     
##                                               "Stratified by Pandemic"
##  ""                                            "p"      "test" "Missing"
##   "n"                                          ""       ""     "    "   
##   "Age_0 (mean (SD))"                          "<0.001" ""     " 0.0"   
##   "Sex_0 = M (%)"                              "<0.001" ""     " 0.0"   
##   "BMI_0 (mean (SD))"                          " 0.378" ""     " 0.5"   
##   "Ethnicity_0 = White (%)"                    " 0.083" ""     " 0.0"   
##   "Relationship_status_0 (%)"                  " 0.004" ""     " 0.0"   
##   "   Divorced"                                ""       ""     "    "   
##   "   Married"                                 ""       ""     "    "   
##   "   Separated"                               ""       ""     "    "   
##   "   Single"                                  ""       ""     "    "   
##   "   Widowed"                                 ""       ""     "    "   
##   "Education4_0 (%)"                           "<0.001" ""     " 0.0"   
##   "   College Degree or Higher"                ""       ""     "    "   
##   "   High School Diploma"                     ""       ""     "    "   
##   "   Less than High School Diploma"           ""       ""     "    "   
##   "   Some College"                            ""       ""     "    "   
##   "Income_Level_0 (%)"                         "<0.001" ""     " 3.6"   
##   "   <$20k"                                   ""       ""     "    "   
##   "   >$150k"                                  ""       ""     "    "   
##   "   $100-150k"                               ""       ""     "    "   
##   "   $20-50k"                                 ""       ""     "    "   
##   "   $50-100k"                                ""       ""     "    "   
##   "Living_status_0 (%)"                        " 0.421" ""     " 0.0"   
##   "   Apartment/Condo/Townhome"                ""       ""     "    "   
##   "   Assisted Living"                         ""       ""     "    "   
##   "   House"                                   ""       ""     "    "   
##   "   Other"                                   ""       ""     "    "   
##   "Alcohol_0 (%)"                              " 0.914" ""     " 3.0"   
##   "   Non-drinker"                             ""       ""     "    "   
##   "   Occasional drinker"                      ""       ""     "    "   
##   "   Regular drinker (at least once a month)" ""       ""     "    "   
##   "Smoking_Status_0 (%)"                       " 0.331" ""     " 0.5"   
##   "   Daily Smoker"                            ""       ""     "    "   
##   "   Former Smoker"                           ""       ""     "    "   
##   "   Never Smoked"                            ""       ""     "    "   
##   "   Occasional Smoker"                       ""       ""     "    "   
##   "CESD_10_0 (mean (SD))"                      " 0.006" ""     " 0.3"   
##   "Anxiety_0 = Yes (%)"                        " 0.624" ""     " 0.1"   
##   "Mood_Disord_0 = Yes (%)"                    " 0.940" ""     " 0.1"   
##   "Pet_Owner_0 = Yes (%)"                      " 0.003" ""     " 0.4"   
##   "PASE_TOTAL_0 (mean (SD))"                   "<0.001" ""     "19.2"   
##   "MAT_Score_0 (mean (SD))"                    " 0.168" ""     " 0.0"   
##   "RVLT_Immediate_Score_0 (mean (SD))"         "<0.001" ""     " 0.0"   
##   "Animal_Fluency_Lenient_0 (mean (SD))"       " 0.006" ""     " 0.0"   
##   "RSTLS_Sleep_0 (mean (SD))"                  " 0.804" ""     " 0.2"

16.3) Full Adjusted Model: Time x Pandemic effects on cognition, physical activity, and sleep duration (not adjusting for baseline)

All models use normalized cognitive scores.Each model is adjusted for baseline age, sex, education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, and baseline PASE score

16.3.1) RVLT Immediate Recall

16.3.1.1) Model

modelRVLT_imm_8_trun<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                         (1|ID), data= truncated.data_long)
summary(modelRVLT_imm_8_trun)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## RVLT_Immediate_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: truncated.data_long
## 
## REML criterion at convergence: 109191.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6122 -0.5799 -0.0477  0.5194  4.5803 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.212    2.283   
##  Residual             8.380    2.895   
## Number of obs: 20522, groups:  ID, 6987
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             7.893e+00  4.680e-01
## timefactor2                                             7.000e-01  6.657e-02
## timefactor3                                             1.013e+00  6.725e-02
## PandemicFU2 data collected before COVID-19              4.511e-01  8.995e-02
## Age                                                     1.556e-02  4.403e-03
## SexM                                                   -7.664e-01  7.666e-02
## EducationHigh School Diploma                            1.917e-01  1.076e-01
## EducationLess than High School Diploma                  6.156e-01  1.525e-01
## EducationSome College                                   2.297e-01  1.337e-01
## EthnicityWhite                                          1.219e+00  2.132e-01
## IncomeLevel>$150k                                       8.362e-01  1.968e-01
## IncomeLevel$100-150k                                    4.443e-01  1.599e-01
## IncomeLevel$20-50k                                      8.045e-02  1.078e-01
## IncomeLevel$50-100k                                     5.144e-01  1.159e-01
## BMI                                                    -1.805e-02  7.266e-03
## CESD.20.1                                              -3.680e-02  8.659e-03
## SmokingStatusFormer Smoker                              3.629e-01  1.467e-01
## SmokingStatusNever Smoked                               5.008e-01  1.526e-01
## SmokingStatusOccasional Smoker                          4.226e-01  2.895e-01
## RelationshipstatusMarried                               1.075e-01  1.260e-01
## RelationshipstatusSeparated                            -1.496e-01  2.440e-01
## RelationshipstatusSingle                               -4.828e-02  1.709e-01
## RelationshipstatusWidowed                              -6.016e-02  1.750e-01
## LivingstatusAssisted Living                            -3.970e-01  5.315e-01
## LivingstatusHouse                                       2.161e-02  1.150e-01
## LivingstatusOther                                      -5.238e-01  4.278e-01
## AnxietyYes                                             -4.922e-02  1.507e-01
## MoodDisordYes                                          -8.521e-02  1.087e-01
## Chronicconditions                                      -3.093e-02  1.776e-02
## PASE_TOTALbaseline                                      2.891e-03  5.101e-04
## timefactor2:PandemicFU2 data collected before COVID-19 -1.758e-01  9.916e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -2.584e-01  9.967e-02
##                                                                df t value
## (Intercept)                                             7.013e+03  16.865
## timefactor2                                             1.358e+04  10.515
## timefactor3                                             1.366e+04  15.069
## PandemicFU2 data collected before COVID-19              1.554e+04   5.015
## Age                                                     6.928e+03   3.535
## SexM                                                    6.927e+03  -9.998
## EducationHigh School Diploma                            6.932e+03   1.781
## EducationLess than High School Diploma                  6.992e+03   4.037
## EducationSome College                                   6.898e+03   1.718
## EthnicityWhite                                          6.898e+03   5.719
## IncomeLevel>$150k                                       6.935e+03   4.249
## IncomeLevel$100-150k                                    6.909e+03   2.779
## IncomeLevel$20-50k                                      6.933e+03   0.747
## IncomeLevel$50-100k                                     6.932e+03   4.439
## BMI                                                     6.916e+03  -2.485
## CESD.20.1                                               6.925e+03  -4.250
## SmokingStatusFormer Smoker                              6.936e+03   2.474
## SmokingStatusNever Smoked                               6.936e+03   3.282
## SmokingStatusOccasional Smoker                          6.862e+03   1.460
## RelationshipstatusMarried                               6.935e+03   0.853
## RelationshipstatusSeparated                             6.965e+03  -0.613
## RelationshipstatusSingle                                6.935e+03  -0.283
## RelationshipstatusWidowed                               6.939e+03  -0.344
## LivingstatusAssisted Living                             6.931e+03  -0.747
## LivingstatusHouse                                       6.938e+03   0.188
## LivingstatusOther                                       6.855e+03  -1.224
## AnxietyYes                                              6.912e+03  -0.327
## MoodDisordYes                                           6.924e+03  -0.784
## Chronicconditions                                       6.920e+03  -1.741
## PASE_TOTALbaseline                                      6.920e+03   5.668
## timefactor2:PandemicFU2 data collected before COVID-19  1.358e+04  -1.773
## timefactor3:PandemicFU2 data collected before COVID-19  1.362e+04  -2.593
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## timefactor3                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             5.36e-07 ***
## Age                                                    0.000411 ***
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                           0.074885 .  
## EducationLess than High School Diploma                 5.47e-05 ***
## EducationSome College                                  0.085816 .  
## EthnicityWhite                                         1.12e-08 ***
## IncomeLevel>$150k                                      2.18e-05 ***
## IncomeLevel$100-150k                                   0.005468 ** 
## IncomeLevel$20-50k                                     0.455385    
## IncomeLevel$50-100k                                    9.18e-06 ***
## BMI                                                    0.012995 *  
## CESD.20.1                                              2.16e-05 ***
## SmokingStatusFormer Smoker                             0.013388 *  
## SmokingStatusNever Smoked                              0.001037 ** 
## SmokingStatusOccasional Smoker                         0.144423    
## RelationshipstatusMarried                              0.393545    
## RelationshipstatusSeparated                            0.539819    
## RelationshipstatusSingle                               0.777566    
## RelationshipstatusWidowed                              0.731066    
## LivingstatusAssisted Living                            0.455120    
## LivingstatusHouse                                      0.850865    
## LivingstatusOther                                      0.220882    
## AnxietyYes                                             0.743904    
## MoodDisordYes                                          0.432980    
## Chronicconditions                                      0.081681 .  
## PASE_TOTALbaseline                                     1.50e-08 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.076316 .  
## timefactor3:PandemicFU2 data collected before COVID-19 0.009525 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.3.1.2) Estimated marginal means

lsmeans(modelRVLT_imm_8_trun, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.453406 0.2346745 Inf  8.993453   9.91336
##  FU2 data collected before COVID-19  9.904496 0.2381181 Inf  9.437793  10.37120
## 
## timefactor = 2:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.153375 0.2347814 Inf  9.693212  10.61354
##  FU2 data collected before COVID-19 10.428696 0.2382731 Inf  9.961689  10.89570
## 
## timefactor = 3:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.466794 0.2349867 Inf 10.006228  10.92736
##  FU2 data collected before COVID-19 10.659448 0.2382748 Inf 10.192438  11.12646
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_imm_8_trun, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.4510900 0.08994764 Inf  -5.015  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.2753214 0.09072466 Inf  -3.035  0.0024
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1926541 0.09130995 Inf  -2.110  0.0349
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_imm_8_trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.4510900 0.08994764 Inf -0.6273842 -0.27479591
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.2753214 0.09072466 Inf -0.4531384 -0.09750432
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.1926541 0.09130995 Inf -0.3716183 -0.01368988
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.3.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_8trun <- summary(lsmeans(modelRVLT_imm_8_trun, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTimmediate_lsmeans_8trun$Time<-NA
RVLTimmediate_lsmeans_8trun$Time[RVLTimmediate_lsmeans_8trun$timefactor==1]<-"Baseline"
RVLTimmediate_lsmeans_8trun$Time[RVLTimmediate_lsmeans_8trun$timefactor==2]<-"Follow-up 1"
RVLTimmediate_lsmeans_8trun$Time[RVLTimmediate_lsmeans_8trun$timefactor==3]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_8trun, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + 
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "RVLT Immediate Score", title = "RVLT Immediate Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

16.3.2) RVLT Delayed Recall

16.3.2.1) Model

modelRVLT_del_8trun<- lmer(RVLT_Delayed_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= truncated.data_long)
summary(modelRVLT_del_8trun)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: truncated.data_long
## 
## REML criterion at convergence: 107532.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8255 -0.5652 -0.0365  0.5143  5.0735 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.571    2.360   
##  Residual             7.698    2.775   
## Number of obs: 20402, groups:  ID, 6987
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             7.850e+00  4.725e-01
## timefactor2                                             5.563e-01  6.405e-02
## timefactor3                                             1.143e+00  6.467e-02
## PandemicFU2 data collected before COVID-19              3.733e-01  8.893e-02
## Age                                                     2.642e-02  4.444e-03
## SexM                                                   -6.548e-01  7.735e-02
## EducationHigh School Diploma                            3.673e-01  1.086e-01
## EducationLess than High School Diploma                  5.761e-01  1.541e-01
## EducationSome College                                   3.710e-01  1.348e-01
## EthnicityWhite                                          1.110e+00  2.158e-01
## IncomeLevel>$150k                                       6.652e-01  1.985e-01
## IncomeLevel$100-150k                                    5.232e-01  1.613e-01
## IncomeLevel$20-50k                                      2.135e-01  1.088e-01
## IncomeLevel$50-100k                                     5.715e-01  1.169e-01
## BMI                                                    -1.843e-02  7.330e-03
## CESD.20.1                                              -3.674e-02  8.742e-03
## SmokingStatusFormer Smoker                              9.877e-02  1.480e-01
## SmokingStatusNever Smoked                               3.429e-01  1.539e-01
## SmokingStatusOccasional Smoker                          3.115e-01  2.924e-01
## RelationshipstatusMarried                              -4.107e-02  1.272e-01
## RelationshipstatusSeparated                            -1.311e-01  2.463e-01
## RelationshipstatusSingle                               -2.088e-01  1.724e-01
## RelationshipstatusWidowed                              -1.076e-01  1.767e-01
## LivingstatusAssisted Living                            -1.210e+00  5.368e-01
## LivingstatusHouse                                      -1.293e-03  1.160e-01
## LivingstatusOther                                      -2.917e-01  4.325e-01
## AnxietyYes                                             -2.939e-02  1.521e-01
## MoodDisordYes                                          -1.189e-01  1.096e-01
## Chronicconditions                                      -4.024e-02  1.792e-02
## PASE_TOTALbaseline                                      3.072e-03  5.148e-04
## timefactor2:PandemicFU2 data collected before COVID-19 -6.747e-03  9.535e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -2.638e-01  9.580e-02
##                                                                df t value
## (Intercept)                                             7.027e+03  16.614
## timefactor2                                             1.350e+04   8.686
## timefactor3                                             1.356e+04  17.682
## PandemicFU2 data collected before COVID-19              1.482e+04   4.198
## Age                                                     6.939e+03   5.946
## SexM                                                    6.929e+03  -8.466
## EducationHigh School Diploma                            6.941e+03   3.383
## EducationLess than High School Diploma                  7.028e+03   3.738
## EducationSome College                                   6.891e+03   2.752
## EthnicityWhite                                          6.974e+03   5.146
## IncomeLevel>$150k                                       6.931e+03   3.351
## IncomeLevel$100-150k                                    6.913e+03   3.244
## IncomeLevel$20-50k                                      6.942e+03   1.963
## IncomeLevel$50-100k                                     6.935e+03   4.887
## BMI                                                     6.912e+03  -2.514
## CESD.20.1                                               6.941e+03  -4.203
## SmokingStatusFormer Smoker                              6.927e+03   0.668
## SmokingStatusNever Smoked                               6.928e+03   2.228
## SmokingStatusOccasional Smoker                          6.887e+03   1.065
## RelationshipstatusMarried                               6.946e+03  -0.323
## RelationshipstatusSeparated                             6.982e+03  -0.532
## RelationshipstatusSingle                                6.934e+03  -1.211
## RelationshipstatusWidowed                               6.950e+03  -0.609
## LivingstatusAssisted Living                             6.962e+03  -2.254
## LivingstatusHouse                                       6.952e+03  -0.011
## LivingstatusOther                                       6.903e+03  -0.674
## AnxietyYes                                              6.920e+03  -0.193
## MoodDisordYes                                           6.920e+03  -1.085
## Chronicconditions                                       6.923e+03  -2.245
## PASE_TOTALbaseline                                      6.930e+03   5.967
## timefactor2:PandemicFU2 data collected before COVID-19  1.349e+04  -0.071
## timefactor3:PandemicFU2 data collected before COVID-19  1.352e+04  -2.754
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## timefactor3                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             2.71e-05 ***
## Age                                                    2.89e-09 ***
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                           0.000722 ***
## EducationLess than High School Diploma                 0.000187 ***
## EducationSome College                                  0.005941 ** 
## EthnicityWhite                                         2.73e-07 ***
## IncomeLevel>$150k                                      0.000810 ***
## IncomeLevel$100-150k                                   0.001186 ** 
## IncomeLevel$20-50k                                     0.049638 *  
## IncomeLevel$50-100k                                    1.04e-06 ***
## BMI                                                    0.011947 *  
## CESD.20.1                                              2.67e-05 ***
## SmokingStatusFormer Smoker                             0.504433    
## SmokingStatusNever Smoked                              0.025898 *  
## SmokingStatusOccasional Smoker                         0.286748    
## RelationshipstatusMarried                              0.746819    
## RelationshipstatusSeparated                            0.594524    
## RelationshipstatusSingle                               0.225989    
## RelationshipstatusWidowed                              0.542607    
## LivingstatusAssisted Living                            0.024220 *  
## LivingstatusHouse                                      0.991112    
## LivingstatusOther                                      0.500090    
## AnxietyYes                                             0.846740    
## MoodDisordYes                                          0.278125    
## Chronicconditions                                      0.024786 *  
## PASE_TOTALbaseline                                     2.54e-09 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.943589    
## timefactor3:PandemicFU2 data collected before COVID-19 0.005894 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.3.2.2) Estimated marginal means

lsmeans(modelRVLT_del_8trun, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.79743 0.2367786 Inf  9.333353  10.26151
##  FU2 data collected before COVID-19 10.17072 0.2401909 Inf  9.699954  10.64148
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.35377 0.2369809 Inf  9.889297  10.81824
##  FU2 data collected before COVID-19 10.72031 0.2404243 Inf 10.249090  11.19154
## 
## timefactor = 3:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.94086 0.2371645 Inf 10.476023  11.40569
##  FU2 data collected before COVID-19 11.05030 0.2404106 Inf 10.579107  11.52150
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_del_8trun, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.3732888 0.08892997 Inf  -4.198  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.3665420 0.08996807 Inf  -4.074  <.0001
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1094461 0.09049012 Inf  -1.209  0.2265
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_del_8trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.3732888 0.08892997 Inf -0.5475884 -0.19898931
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.3665420 0.08996807 Inf -0.5428762 -0.19020787
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.1094461 0.09049012 Inf -0.2868035  0.06791125
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.3.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_8trun <- summary(lsmeans(modelRVLT_del_8trun, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTdelayed_lsmeans_8trun$Time<-NA
RVLTdelayed_lsmeans_8trun$Time[RVLTdelayed_lsmeans_8trun$timefactor==1]<-"Baseline"
RVLTdelayed_lsmeans_8trun$Time[RVLTdelayed_lsmeans_8trun$timefactor==2]<-"Follow-up 1"
RVLTdelayed_lsmeans_8trun$Time[RVLTdelayed_lsmeans_8trun$timefactor==3]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_8trun, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + 
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "RVLT Delayed Score", title = "RVLT Delayed Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

16.3.3) Mental Alteration Test

16.3.3.1) Model

modelMAT_8trun<- lmer(MAT_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                    (1|ID), data= truncated.data_long)
summary(modelMAT_8trun)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: truncated.data_long
## 
## REML criterion at convergence: 103602.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9324 -0.4800 -0.0217  0.4226  4.7815 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.907    2.215   
##  Residual             8.093    2.845   
## Number of obs: 19603, groups:  ID, 6987
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             9.872e+00  4.618e-01
## timefactor2                                             1.090e+00  6.717e-02
## timefactor3                                            -2.731e-01  6.761e-02
## PandemicFU2 data collected before COVID-19             -3.908e-03  8.799e-02
## Age                                                    -2.577e-03  4.348e-03
## SexM                                                   -1.096e+00  7.553e-02
## EducationHigh School Diploma                            5.325e-03  1.061e-01
## EducationLess than High School Diploma                 -2.912e-02  1.513e-01
## EducationSome College                                   7.804e-02  1.315e-01
## EthnicityWhite                                          1.380e+00  2.110e-01
## IncomeLevel>$150k                                       7.419e-01  1.938e-01
## IncomeLevel$100-150k                                    8.372e-01  1.574e-01
## IncomeLevel$20-50k                                      3.364e-01  1.063e-01
## IncomeLevel$50-100k                                     6.873e-01  1.143e-01
## BMI                                                    -2.270e-02  7.150e-03
## CESD.20.1                                              -4.726e-02  8.533e-03
## SmokingStatusFormer Smoker                              1.741e-01  1.443e-01
## SmokingStatusNever Smoked                               1.158e-01  1.501e-01
## SmokingStatusOccasional Smoker                          1.618e-01  2.852e-01
## RelationshipstatusMarried                               1.802e-01  1.243e-01
## RelationshipstatusSeparated                            -1.733e-01  2.399e-01
## RelationshipstatusSingle                                2.984e-01  1.685e-01
## RelationshipstatusWidowed                              -1.861e-01  1.730e-01
## LivingstatusAssisted Living                            -4.556e-01  5.245e-01
## LivingstatusHouse                                      -1.610e-01  1.135e-01
## LivingstatusOther                                      -2.618e-01  4.236e-01
## AnxietyYes                                              1.170e-01  1.483e-01
## MoodDisordYes                                           3.088e-01  1.069e-01
## Chronicconditions                                      -6.526e-02  1.752e-02
## PASE_TOTALbaseline                                     -7.932e-04  5.020e-04
## timefactor2:PandemicFU2 data collected before COVID-19 -2.752e-01  1.001e-01
## timefactor3:PandemicFU2 data collected before COVID-19  8.568e-02  9.988e-02
##                                                                df t value
## (Intercept)                                             6.993e+03  21.376
## timefactor2                                             1.291e+04  16.221
## timefactor3                                             1.295e+04  -4.040
## PandemicFU2 data collected before COVID-19              1.511e+04  -0.044
## Age                                                     6.928e+03  -0.593
## SexM                                                    6.874e+03 -14.507
## EducationHigh School Diploma                            6.893e+03   0.050
## EducationLess than High School Diploma                  7.072e+03  -0.192
## EducationSome College                                   6.815e+03   0.594
## EthnicityWhite                                          6.951e+03   6.541
## IncomeLevel>$150k                                       6.864e+03   3.829
## IncomeLevel$100-150k                                    6.841e+03   5.321
## IncomeLevel$20-50k                                      6.913e+03   3.163
## IncomeLevel$50-100k                                     6.895e+03   6.015
## BMI                                                     6.832e+03  -3.174
## CESD.20.1                                               6.871e+03  -5.539
## SmokingStatusFormer Smoker                              6.836e+03   1.207
## SmokingStatusNever Smoked                               6.837e+03   0.772
## SmokingStatusOccasional Smoker                          6.790e+03   0.567
## RelationshipstatusMarried                               6.910e+03   1.450
## RelationshipstatusSeparated                             6.875e+03  -0.722
## RelationshipstatusSingle                                6.892e+03   1.771
## RelationshipstatusWidowed                               6.955e+03  -1.076
## LivingstatusAssisted Living                             6.888e+03  -0.869
## LivingstatusHouse                                       6.926e+03  -1.419
## LivingstatusOther                                       6.912e+03  -0.618
## AnxietyYes                                              6.843e+03   0.789
## MoodDisordYes                                           6.836e+03   2.889
## Chronicconditions                                       6.892e+03  -3.724
## PASE_TOTALbaseline                                      6.841e+03  -1.580
## timefactor2:PandemicFU2 data collected before COVID-19  1.290e+04  -2.750
## timefactor3:PandemicFU2 data collected before COVID-19  1.288e+04   0.858
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## timefactor3                                            5.37e-05 ***
## PandemicFU2 data collected before COVID-19             0.964574    
## Age                                                    0.553493    
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                           0.959980    
## EducationLess than High School Diploma                 0.847361    
## EducationSome College                                  0.552816    
## EthnicityWhite                                         6.54e-11 ***
## IncomeLevel>$150k                                      0.000130 ***
## IncomeLevel$100-150k                                   1.07e-07 ***
## IncomeLevel$20-50k                                     0.001566 ** 
## IncomeLevel$50-100k                                    1.90e-09 ***
## BMI                                                    0.001508 ** 
## CESD.20.1                                              3.16e-08 ***
## SmokingStatusFormer Smoker                             0.227651    
## SmokingStatusNever Smoked                              0.440239    
## SmokingStatusOccasional Smoker                         0.570396    
## RelationshipstatusMarried                              0.147059    
## RelationshipstatusSeparated                            0.470146    
## RelationshipstatusSingle                               0.076539 .  
## RelationshipstatusWidowed                              0.281992    
## LivingstatusAssisted Living                            0.385105    
## LivingstatusHouse                                      0.156030    
## LivingstatusOther                                      0.536627    
## AnxietyYes                                             0.430189    
## MoodDisordYes                                          0.003881 ** 
## Chronicconditions                                      0.000197 ***
## PASE_TOTALbaseline                                     0.114129    
## timefactor2:PandemicFU2 data collected before COVID-19 0.005972 ** 
## timefactor3:PandemicFU2 data collected before COVID-19 0.391014    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.3.3.2) Estimated marginal means

lsmeans(modelMAT_8trun, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.367255 0.2316145 Inf  8.913299  9.821211
##  FU2 data collected before COVID-19  9.363347 0.2350058 Inf  8.902744  9.823950
## 
## timefactor = 2:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.456874 0.2323708 Inf 10.001436 10.912312
##  FU2 data collected before COVID-19 10.177789 0.2357663 Inf  9.715696 10.639883
## 
## timefactor = 3:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.094120 0.2321972 Inf  8.639022  9.549219
##  FU2 data collected before COVID-19  9.175889 0.2356319 Inf  8.714059  9.637719
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelMAT_8trun, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##   0.00390799 0.08798809 Inf   0.044  0.9646
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##   0.27908487 0.09165936 Inf   3.045  0.0023
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.08176819 0.09144812 Inf  -0.894  0.3712
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelMAT_8trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df   asymp.LCL asymp.UCL
##   0.00390799 0.08798809 Inf -0.16854550 0.1763615
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df   asymp.LCL asymp.UCL
##   0.27908487 0.09165936 Inf  0.09943582 0.4587339
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df   asymp.LCL asymp.UCL
##  -0.08176819 0.09144812 Inf -0.26100321 0.0974668
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.3.3.3) Graph of estimated marginal means

MAT_lsmeans_8trun <- summary(lsmeans(modelMAT_8trun, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.

MAT_lsmeans_8trun$Time<-NA
MAT_lsmeans_8trun$Time[MAT_lsmeans_8trun$timefactor==1]<-"Baseline"
MAT_lsmeans_8trun$Time[MAT_lsmeans_8trun$timefactor==2]<-"Follow-up 1"
MAT_lsmeans_8trun$Time[MAT_lsmeans_8trun$timefactor==3]<-"Follow-up 2"
ggplot(MAT_lsmeans_8trun, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + 
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "MAT Score", title = "Mental Alteration Test Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

16.3.4) Animal Fluency

16.3.4.1) Model

modelAnimals_8trun<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= truncated.data_long)
summary(modelAnimals_8trun)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## Animal_Fluency_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: truncated.data_long
## 
## REML criterion at convergence: 96464.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.3556 -0.5650 -0.0117  0.5536  4.4684 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.871    2.207   
##  Residual             3.648    1.910   
## Number of obs: 20626, groups:  ID, 6987
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             8.979e+00  4.065e-01
## timefactor2                                            -1.427e-01  4.386e-02
## timefactor3                                            -5.172e-02  4.435e-02
## PandemicFU2 data collected before COVID-19              2.025e-01  7.142e-02
## Age                                                    -2.083e-03  3.828e-03
## SexM                                                   -2.582e-01  6.667e-02
## EducationHigh School Diploma                            3.276e-01  9.359e-02
## EducationLess than High School Diploma                  3.278e-01  1.324e-01
## EducationSome College                                   2.877e-01  1.163e-01
## EthnicityWhite                                          1.379e+00  1.855e-01
## IncomeLevel>$150k                                       5.279e-01  1.711e-01
## IncomeLevel$100-150k                                    4.182e-01  1.391e-01
## IncomeLevel$20-50k                                      5.155e-02  9.370e-02
## IncomeLevel$50-100k                                     2.568e-01  1.008e-01
## BMI                                                    -1.644e-02  6.320e-03
## CESD.20.1                                              -3.514e-02  7.533e-03
## SmokingStatusFormer Smoker                              2.922e-01  1.276e-01
## SmokingStatusNever Smoked                               2.416e-01  1.327e-01
## SmokingStatusOccasional Smoker                          4.370e-01  2.522e-01
## RelationshipstatusMarried                              -2.504e-01  1.096e-01
## RelationshipstatusSeparated                            -7.852e-02  2.120e-01
## RelationshipstatusSingle                               -1.519e-01  1.486e-01
## RelationshipstatusWidowed                              -2.843e-01  1.521e-01
## LivingstatusAssisted Living                            -2.298e-01  4.625e-01
## LivingstatusHouse                                       3.577e-01  9.996e-02
## LivingstatusOther                                       2.292e-01  3.724e-01
## AnxietyYes                                             -4.920e-02  1.311e-01
## MoodDisordYes                                           2.641e-01  9.452e-02
## Chronicconditions                                      -7.964e-03  1.545e-02
## PASE_TOTALbaseline                                      1.159e-03  4.436e-04
## timefactor2:PandemicFU2 data collected before COVID-19  8.875e-03  6.527e-02
## timefactor3:PandemicFU2 data collected before COVID-19  4.496e-02  6.564e-02
##                                                                df t value
## (Intercept)                                             7.007e+03  22.087
## timefactor2                                             1.367e+04  -3.255
## timefactor3                                             1.372e+04  -1.166
## PandemicFU2 data collected before COVID-19              1.227e+04   2.836
## Age                                                     6.949e+03  -0.544
## SexM                                                    6.954e+03  -3.872
## EducationHigh School Diploma                            6.962e+03   3.501
## EducationLess than High School Diploma                  6.982e+03   2.475
## EducationSome College                                   6.938e+03   2.473
## EthnicityWhite                                          6.943e+03   7.431
## IncomeLevel>$150k                                       6.955e+03   3.085
## IncomeLevel$100-150k                                    6.948e+03   3.007
## IncomeLevel$20-50k                                      6.954e+03   0.550
## IncomeLevel$50-100k                                     6.955e+03   2.548
## BMI                                                     6.945e+03  -2.601
## CESD.20.1                                               6.959e+03  -4.665
## SmokingStatusFormer Smoker                              6.954e+03   2.291
## SmokingStatusNever Smoked                               6.954e+03   1.821
## SmokingStatusOccasional Smoker                          6.924e+03   1.733
## RelationshipstatusMarried                               6.969e+03  -2.284
## RelationshipstatusSeparated                             6.974e+03  -0.370
## RelationshipstatusSingle                                6.965e+03  -1.022
## RelationshipstatusWidowed                               6.955e+03  -1.869
## LivingstatusAssisted Living                             6.974e+03  -0.497
## LivingstatusHouse                                       6.964e+03   3.578
## LivingstatusOther                                       6.901e+03   0.615
## AnxietyYes                                              6.951e+03  -0.375
## MoodDisordYes                                           6.957e+03   2.794
## Chronicconditions                                       6.945e+03  -0.516
## PASE_TOTALbaseline                                      6.949e+03   2.612
## timefactor2:PandemicFU2 data collected before COVID-19  1.366e+04   0.136
## timefactor3:PandemicFU2 data collected before COVID-19  1.369e+04   0.685
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            0.001138 ** 
## timefactor3                                            0.243588    
## PandemicFU2 data collected before COVID-19             0.004580 ** 
## Age                                                    0.586412    
## SexM                                                   0.000109 ***
## EducationHigh School Diploma                           0.000467 ***
## EducationLess than High School Diploma                 0.013341 *  
## EducationSome College                                  0.013404 *  
## EthnicityWhite                                         1.20e-13 ***
## IncomeLevel>$150k                                      0.002041 ** 
## IncomeLevel$100-150k                                   0.002651 ** 
## IncomeLevel$20-50k                                     0.582215    
## IncomeLevel$50-100k                                    0.010848 *  
## BMI                                                    0.009302 ** 
## CESD.20.1                                              3.15e-06 ***
## SmokingStatusFormer Smoker                             0.021996 *  
## SmokingStatusNever Smoked                              0.068609 .  
## SmokingStatusOccasional Smoker                         0.083147 .  
## RelationshipstatusMarried                              0.022373 *  
## RelationshipstatusSeparated                            0.711183    
## RelationshipstatusSingle                               0.306991    
## RelationshipstatusWidowed                              0.061663 .  
## LivingstatusAssisted Living                            0.619357    
## LivingstatusHouse                                      0.000349 ***
## LivingstatusOther                                      0.538362    
## AnxietyYes                                             0.707469    
## MoodDisordYes                                          0.005215 ** 
## Chronicconditions                                      0.606167    
## PASE_TOTALbaseline                                     0.009023 ** 
## timefactor2:PandemicFU2 data collected before COVID-19 0.891832    
## timefactor3:PandemicFU2 data collected before COVID-19 0.493368    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.3.4.2) Estimated marginal means

lsmeans(modelAnimals_8trun, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  9.750112 0.2031309 Inf  9.351982  10.14824
##  FU2 data collected before COVID-19 9.952642 0.2058940 Inf  9.549097  10.35619
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  9.607374 0.2031899 Inf  9.209129  10.00562
##  FU2 data collected before COVID-19 9.818779 0.2059189 Inf  9.415185  10.22237
## 
## timefactor = 3:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  9.698391 0.2032711 Inf  9.299986  10.09680
##  FU2 data collected before COVID-19 9.945882 0.2059684 Inf  9.542192  10.34957
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelAnimals_8trun, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.2025299 0.07142133 Inf  -2.836  0.0046
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.2114053 0.07170107 Inf  -2.948  0.0032
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.2474918 0.07206497 Inf  -3.434  0.0006
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelAnimals_8trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.2025299 0.07142133 Inf -0.3425131 -0.06254665
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.2114053 0.07170107 Inf -0.3519369 -0.07087383
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.2474918 0.07206497 Inf -0.3887365 -0.10624706
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.3.4.3) Graph of estimated marginal means

Animals_lsmeans_8trun <- summary(lsmeans(modelAnimals_8trun, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.

Animals_lsmeans_8trun$Time<-NA
Animals_lsmeans_8trun$Time[Animals_lsmeans_8trun$timefactor==1]<-"Baseline"
Animals_lsmeans_8trun$Time[Animals_lsmeans_8trun$timefactor==2]<-"Follow-up 1"
Animals_lsmeans_8trun$Time[Animals_lsmeans_8trun$timefactor==3]<-"Follow-up 2"
ggplot(Animals_lsmeans_8trun, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + 
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "Animal Fluency (words)", title = "Animal Fluency Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

16.4) Full Adjusted Model: Time x Pandemic effects on cognition and physical activity ( adjusting for baseline)

All models use normalized cognitive scores. Each model is adjusted for baseline age, sex, education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, baseline PASE score, and baseline cognitive performance

16.4.1) RVLT Immediate Recall

16.4.1.1) Model

modelRVLT_imm_adj10trun<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +
                            (1|ID), data= truncated.data_long_2)
summary(modelRVLT_imm_adj10trun)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## RVLT_Immediate_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +  
##     (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 70686.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6998 -0.5714 -0.0379  0.5273  3.8358 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 3.951    1.988   
##  Residual             7.521    2.743   
## Number of obs: 13535, groups:  ID, 6973
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             7.310e+00  4.650e-01
## timefactor2                                             3.109e-01  6.425e-02
## PandemicFU2 data collected before COVID-19              1.741e-01  8.368e-02
## Age                                                    -1.625e-02  4.367e-03
## SexM                                                   -5.030e-01  7.602e-02
## EducationHigh School Diploma                            2.889e-01  1.063e-01
## EducationLess than High School Diploma                  4.337e-01  1.512e-01
## EducationSome College                                   1.433e-01  1.319e-01
## EthnicityWhite                                          6.392e-01  2.106e-01
## IncomeLevel>$150k                                       3.879e-01  1.947e-01
## IncomeLevel$100-150k                                    2.799e-01  1.578e-01
## IncomeLevel$20-50k                                      1.605e-01  1.064e-01
## IncomeLevel$50-100k                                     4.062e-01  1.145e-01
## BMI                                                    -1.609e-02  7.173e-03
## CESD.10baseline                                        -2.310e-02  8.556e-03
## SmokingStatusFormer Smoker                              2.334e-01  1.450e-01
## SmokingStatusNever Smoked                               3.461e-01  1.508e-01
## SmokingStatusOccasional Smoker                          4.729e-02  2.850e-01
## RelationshipstatusMarried                               3.348e-01  1.245e-01
## RelationshipstatusSeparated                             3.097e-01  2.415e-01
## RelationshipstatusSingle                                2.316e-01  1.689e-01
## RelationshipstatusWidowed                               3.257e-02  1.729e-01
## LivingstatusAssisted Living                            -9.343e-01  5.249e-01
## LivingstatusHouse                                      -3.798e-02  1.136e-01
## LivingstatusOther                                      -5.045e-01  4.209e-01
## AnxietyYes                                              5.017e-02  1.486e-01
## MoodDisordYes                                          -1.114e-01  1.073e-01
## Chronicconditions                                      -2.421e-02  1.754e-02
## PASE_TOTALbaseline                                      2.198e-03  5.041e-04
## RVLT_Immediate_Normedbaseline                           3.554e-01  8.988e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -7.979e-02  9.517e-02
##                                                                df t value
## (Intercept)                                             6.954e+03  15.720
## timefactor2                                             6.830e+03   4.839
## PandemicFU2 data collected before COVID-19              1.203e+04   2.081
## Age                                                     6.915e+03  -3.721
## SexM                                                    6.911e+03  -6.617
## EducationHigh School Diploma                            6.917e+03   2.718
## EducationLess than High School Diploma                  7.040e+03   2.869
## EducationSome College                                   6.839e+03   1.086
## EthnicityWhite                                          6.840e+03   3.035
## IncomeLevel>$150k                                       6.916e+03   1.992
## IncomeLevel$100-150k                                    6.873e+03   1.774
## IncomeLevel$20-50k                                      6.924e+03   1.508
## IncomeLevel$50-100k                                     6.921e+03   3.547
## BMI                                                     6.887e+03  -2.244
## CESD.10baseline                                         6.911e+03  -2.699
## SmokingStatusFormer Smoker                              6.937e+03   1.610
## SmokingStatusNever Smoked                               6.937e+03   2.295
## SmokingStatusOccasional Smoker                          6.807e+03   0.166
## RelationshipstatusMarried                               6.926e+03   2.690
## RelationshipstatusSeparated                             6.989e+03   1.282
## RelationshipstatusSingle                                6.926e+03   1.371
## RelationshipstatusWidowed                               6.931e+03   0.188
## LivingstatusAssisted Living                             6.929e+03  -1.780
## LivingstatusHouse                                       6.923e+03  -0.334
## LivingstatusOther                                       6.794e+03  -1.199
## AnxietyYes                                              6.894e+03   0.338
## MoodDisordYes                                           6.909e+03  -1.038
## Chronicconditions                                       6.902e+03  -1.381
## PASE_TOTALbaseline                                      6.898e+03   4.360
## RVLT_Immediate_Normedbaseline                           6.927e+03  39.546
## timefactor2:PandemicFU2 data collected before COVID-19  6.778e+03  -0.838
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            1.33e-06 ***
## PandemicFU2 data collected before COVID-19             0.037474 *  
## Age                                                    0.000200 ***
## SexM                                                   3.93e-11 ***
## EducationHigh School Diploma                           0.006591 ** 
## EducationLess than High School Diploma                 0.004131 ** 
## EducationSome College                                  0.277388    
## EthnicityWhite                                         0.002417 ** 
## IncomeLevel>$150k                                      0.046385 *  
## IncomeLevel$100-150k                                   0.076170 .  
## IncomeLevel$20-50k                                     0.131549    
## IncomeLevel$50-100k                                    0.000392 ***
## BMI                                                    0.024890 *  
## CESD.10baseline                                        0.006964 ** 
## SmokingStatusFormer Smoker                             0.107355    
## SmokingStatusNever Smoked                              0.021759 *  
## SmokingStatusOccasional Smoker                         0.868203    
## RelationshipstatusMarried                              0.007166 ** 
## RelationshipstatusSeparated                            0.199713    
## RelationshipstatusSingle                               0.170338    
## RelationshipstatusWidowed                              0.850629    
## LivingstatusAssisted Living                            0.075114 .  
## LivingstatusHouse                                      0.738135    
## LivingstatusOther                                      0.230711    
## AnxietyYes                                             0.735738    
## MoodDisordYes                                          0.299306    
## Chronicconditions                                      0.167449    
## PASE_TOTALbaseline                                     1.32e-05 ***
## RVLT_Immediate_Normedbaseline                           < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.401822    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.4.1.2) Estimated marginal means

lsmeans(modelRVLT_imm_adj10trun, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.26540 0.2305354 Inf  9.813563  10.71725
##  FU2 data collected before COVID-19 10.43953 0.2337412 Inf  9.981409  10.89766
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.57632 0.2307399 Inf 10.124082  11.02857
##  FU2 data collected before COVID-19 10.67066 0.2337327 Inf 10.212550  11.12876
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_imm_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.17412870 0.08368398 Inf  -2.081  0.0375
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.09433404 0.08430331 Inf  -1.119  0.2631
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_imm_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.17412870 0.08368398 Inf -0.3381463 -0.01011111
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.09433404 0.08430331 Inf -0.2595655  0.07089741
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.4.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_adj10trun <- summary(lsmeans(modelRVLT_imm_adj10trun, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTimmediate_lsmeans_adj10trun$Time<-NA
RVLTimmediate_lsmeans_adj10trun$Time[RVLTimmediate_lsmeans_adj10trun$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj10trun$Time[RVLTimmediate_lsmeans_adj10trun$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj10trun, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

16.4.2) RVLT Delayed Recall

16.4.2.1) Model

modelRVLT_del_adj10trun<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +
                            (1|ID), data= truncated.data_long_2)
summary(modelRVLT_del_adj10trun)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +  
##     (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 69370.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9097 -0.5557 -0.0353  0.5142  4.0193 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.094    2.023   
##  Residual             6.916    2.630   
## Number of obs: 13415, groups:  ID, 6961
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             6.692e+00  4.627e-01
## timefactor2                                             5.846e-01  6.205e-02
## PandemicFU2 data collected before COVID-19              2.559e-01  8.231e-02
## Age                                                    -8.322e-03  4.343e-03
## SexM                                                   -4.549e-01  7.530e-02
## EducationHigh School Diploma                            2.210e-01  1.056e-01
## EducationLess than High School Diploma                  2.909e-01  1.506e-01
## EducationSome College                                   3.171e-01  1.307e-01
## EthnicityWhite                                          6.681e-01  2.104e-01
## IncomeLevel>$150k                                       2.581e-01  1.929e-01
## IncomeLevel$100-150k                                    1.625e-01  1.566e-01
## IncomeLevel$20-50k                                      1.723e-01  1.057e-01
## IncomeLevel$50-100k                                     4.387e-01  1.136e-01
## BMI                                                    -1.830e-02  7.112e-03
## CESD.10baseline                                        -2.062e-02  8.500e-03
## SmokingStatusFormer Smoker                              1.293e-01  1.436e-01
## SmokingStatusNever Smoked                               3.377e-01  1.494e-01
## SmokingStatusOccasional Smoker                          2.223e-01  2.831e-01
## RelationshipstatusMarried                               3.936e-02  1.237e-01
## RelationshipstatusSeparated                            -1.141e-01  2.398e-01
## RelationshipstatusSingle                               -5.020e-02  1.676e-01
## RelationshipstatusWidowed                              -1.397e-01  1.718e-01
## LivingstatusAssisted Living                            -1.257e+00  5.220e-01
## LivingstatusHouse                                       4.488e-02  1.128e-01
## LivingstatusOther                                      -1.792e-01  4.192e-01
## AnxietyYes                                              5.259e-02  1.476e-01
## MoodDisordYes                                          -1.189e-01  1.064e-01
## Chronicconditions                                      -2.792e-02  1.740e-02
## PASE_TOTALbaseline                                      2.433e-03  5.004e-04
## RVLT_Delayed_Normedbaseline                             3.953e-01  9.114e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -2.551e-01  9.182e-02
##                                                                df t value
## (Intercept)                                             6.969e+03  14.462
## timefactor2                                             6.770e+03   9.421
## PandemicFU2 data collected before COVID-19              1.178e+04   3.109
## Age                                                     6.907e+03  -1.916
## SexM                                                    6.890e+03  -6.042
## EducationHigh School Diploma                            6.904e+03   2.093
## EducationLess than High School Diploma                  7.068e+03   1.932
## EducationSome College                                   6.802e+03   2.427
## EthnicityWhite                                          6.926e+03   3.175
## IncomeLevel>$150k                                       6.891e+03   1.338
## IncomeLevel$100-150k                                    6.858e+03   1.038
## IncomeLevel$20-50k                                      6.917e+03   1.630
## IncomeLevel$50-100k                                     6.901e+03   3.860
## BMI                                                     6.849e+03  -2.573
## CESD.10baseline                                         6.915e+03  -2.426
## SmokingStatusFormer Smoker                              6.902e+03   0.901
## SmokingStatusNever Smoked                               6.902e+03   2.260
## SmokingStatusOccasional Smoker                          6.830e+03   0.785
## RelationshipstatusMarried                               6.890e+03   0.318
## RelationshipstatusSeparated                             6.999e+03  -0.476
## RelationshipstatusSingle                                6.882e+03  -0.300
## RelationshipstatusWidowed                               6.912e+03  -0.813
## LivingstatusAssisted Living                             6.970e+03  -2.407
## LivingstatusHouse                                       6.921e+03   0.398
## LivingstatusOther                                       6.858e+03  -0.428
## AnxietyYes                                              6.870e+03   0.356
## MoodDisordYes                                           6.877e+03  -1.118
## Chronicconditions                                       6.874e+03  -1.604
## PASE_TOTALbaseline                                      6.889e+03   4.863
## RVLT_Delayed_Normedbaseline                             6.894e+03  43.370
## timefactor2:PandemicFU2 data collected before COVID-19  6.710e+03  -2.778
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             0.001883 ** 
## Age                                                    0.055388 .  
## SexM                                                   1.60e-09 ***
## EducationHigh School Diploma                           0.036387 *  
## EducationLess than High School Diploma                 0.053383 .  
## EducationSome College                                  0.015265 *  
## EthnicityWhite                                         0.001505 ** 
## IncomeLevel>$150k                                      0.181073    
## IncomeLevel$100-150k                                   0.299447    
## IncomeLevel$20-50k                                     0.103095    
## IncomeLevel$50-100k                                    0.000114 ***
## BMI                                                    0.010114 *  
## CESD.10baseline                                        0.015285 *  
## SmokingStatusFormer Smoker                             0.367755    
## SmokingStatusNever Smoked                              0.023831 *  
## SmokingStatusOccasional Smoker                         0.432412    
## RelationshipstatusMarried                              0.750375    
## RelationshipstatusSeparated                            0.634240    
## RelationshipstatusSingle                               0.764487    
## RelationshipstatusWidowed                              0.416002    
## LivingstatusAssisted Living                            0.016090 *  
## LivingstatusHouse                                      0.690858    
## LivingstatusOther                                      0.668932    
## AnxietyYes                                             0.721555    
## MoodDisordYes                                          0.263712    
## Chronicconditions                                      0.108660    
## PASE_TOTALbaseline                                     1.18e-06 ***
## RVLT_Delayed_Normedbaseline                             < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.005487 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.4.2.2) Estimated marginal means

lsmeans(modelRVLT_del_adj10trun, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.42374 0.2293039 Inf  9.974309  10.87316
##  FU2 data collected before COVID-19 10.67961 0.2324530 Inf 10.224012  11.13521
## 
## timefactor = 2:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.00833 0.2294950 Inf 10.558528  11.45813
##  FU2 data collected before COVID-19 11.00914 0.2324254 Inf 10.553596  11.46469
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_del_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.25587524 0.08230715 Inf  -3.109  0.0019
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.00081211 0.08287378 Inf  -0.010  0.9922
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_del_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.25587524 0.08230715 Inf -0.4171943 -0.09455618
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.00081211 0.08287378 Inf -0.1632417  0.16161751
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.4.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_adj10trun <- summary(lsmeans(modelRVLT_del_adj10trun, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTdelayed_lsmeans_adj10trun$Time<-NA
RVLTdelayed_lsmeans_adj10trun$Time[RVLTdelayed_lsmeans_adj10trun$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj10trun$Time[RVLTdelayed_lsmeans_adj10trun$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj10trun, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

16.4.3) Mental Alteration Test

16.4.3.1) Model

modelMAT_adj10trun<- lmer(MAT_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + MAT_Normedbaseline +
                      (1|ID), data= truncated.data_long_2)
summary(modelMAT_adj10trun)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + MAT_Normedbaseline +      (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 66278.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3069 -0.5275 -0.0879  0.3683  4.7473 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.248    1.499   
##  Residual             9.083    3.014   
## Number of obs: 12616, groups:  ID, 6874
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             7.818e+00  4.569e-01
## timefactor2                                            -1.367e+00  7.348e-02
## PandemicFU2 data collected before COVID-19             -3.648e-01  8.637e-02
## Age                                                    -1.199e-02  4.239e-03
## SexM                                                   -1.227e+00  7.340e-02
## EducationHigh School Diploma                           -3.722e-02  1.031e-01
## EducationLess than High School Diploma                 -1.354e-01  1.492e-01
## EducationSome College                                  -2.331e-02  1.270e-01
## EthnicityWhite                                          9.034e-01  2.063e-01
## IncomeLevel>$150k                                       1.273e-01  1.881e-01
## IncomeLevel$100-150k                                    1.462e-01  1.526e-01
## IncomeLevel$20-50k                                      1.663e-01  1.036e-01
## IncomeLevel$50-100k                                     1.509e-01  1.114e-01
## BMI                                                    -1.323e-02  6.920e-03
## CESD.10baseline                                        -2.175e-02  8.296e-03
## SmokingStatusFormer Smoker                              8.338e-03  1.396e-01
## SmokingStatusNever Smoked                              -8.297e-02  1.452e-01
## SmokingStatusOccasional Smoker                          1.660e-01  2.751e-01
## RelationshipstatusMarried                              -4.425e-03  1.210e-01
## RelationshipstatusSeparated                            -6.076e-02  2.328e-01
## RelationshipstatusSingle                                5.469e-01  1.637e-01
## RelationshipstatusWidowed                              -1.947e-01  1.689e-01
## LivingstatusAssisted Living                            -1.228e-01  5.100e-01
## LivingstatusHouse                                      -1.599e-01  1.106e-01
## LivingstatusOther                                      -1.830e-01  4.123e-01
## AnxietyYes                                              8.310e-02  1.435e-01
## MoodDisordYes                                           1.138e-01  1.035e-01
## Chronicconditions                                      -3.578e-02  1.704e-02
## PASE_TOTALbaseline                                     -5.028e-04  4.859e-04
## MAT_Normedbaseline                                      4.422e-01  9.591e-03
## timefactor2:PandemicFU2 data collected before COVID-19  3.692e-01  1.087e-01
##                                                                df t value
## (Intercept)                                             6.718e+03  17.112
## timefactor2                                             6.458e+03 -18.600
## PandemicFU2 data collected before COVID-19              1.219e+04  -4.224
## Age                                                     6.692e+03  -2.828
## SexM                                                    6.612e+03 -16.714
## EducationHigh School Diploma                            6.615e+03  -0.361
## EducationLess than High School Diploma                  6.833e+03  -0.908
## EducationSome College                                   6.532e+03  -0.184
## EthnicityWhite                                          6.702e+03   4.378
## IncomeLevel>$150k                                       6.589e+03   0.677
## IncomeLevel$100-150k                                    6.576e+03   0.958
## IncomeLevel$20-50k                                      6.669e+03   1.606
## IncomeLevel$50-100k                                     6.629e+03   1.355
## BMI                                                     6.537e+03  -1.912
## CESD.10baseline                                         6.592e+03  -2.621
## SmokingStatusFormer Smoker                              6.546e+03   0.060
## SmokingStatusNever Smoked                               6.549e+03  -0.571
## SmokingStatusOccasional Smoker                          6.411e+03   0.603
## RelationshipstatusMarried                               6.673e+03  -0.037
## RelationshipstatusSeparated                             6.669e+03  -0.261
## RelationshipstatusSingle                                6.649e+03   3.341
## RelationshipstatusWidowed                               6.733e+03  -1.152
## LivingstatusAssisted Living                             6.525e+03  -0.241
## LivingstatusHouse                                       6.683e+03  -1.446
## LivingstatusOther                                       6.658e+03  -0.444
## AnxietyYes                                              6.593e+03   0.579
## MoodDisordYes                                           6.558e+03   1.100
## Chronicconditions                                       6.630e+03  -2.100
## PASE_TOTALbaseline                                      6.563e+03  -1.035
## MAT_Normedbaseline                                      6.662e+03  46.108
## timefactor2:PandemicFU2 data collected before COVID-19  6.404e+03   3.396
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             2.42e-05 ***
## Age                                                    0.004693 ** 
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                           0.718245    
## EducationLess than High School Diploma                 0.364045    
## EducationSome College                                  0.854364    
## EthnicityWhite                                         1.22e-05 ***
## IncomeLevel>$150k                                      0.498705    
## IncomeLevel$100-150k                                   0.338226    
## IncomeLevel$20-50k                                     0.108364    
## IncomeLevel$50-100k                                    0.175549    
## BMI                                                    0.055902 .  
## CESD.10baseline                                        0.008775 ** 
## SmokingStatusFormer Smoker                             0.952379    
## SmokingStatusNever Smoked                              0.567860    
## SmokingStatusOccasional Smoker                         0.546266    
## RelationshipstatusMarried                              0.970818    
## RelationshipstatusSeparated                            0.794072    
## RelationshipstatusSingle                               0.000839 ***
## RelationshipstatusWidowed                              0.249266    
## LivingstatusAssisted Living                            0.809692    
## LivingstatusHouse                                      0.148248    
## LivingstatusOther                                      0.657210    
## AnxietyYes                                             0.562654    
## MoodDisordYes                                          0.271302    
## Chronicconditions                                      0.035794 *  
## PASE_TOTALbaseline                                     0.300789    
## MAT_Normedbaseline                                      < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.000689 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.4.3.2) Estimated marginal means

lsmeans(modelMAT_adj10trun, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.875132 0.2254903 Inf 10.433179 11.317085
##  FU2 data collected before COVID-19 10.510338 0.2286037 Inf 10.062283 10.958393
## 
## timefactor = 2:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.508377 0.2251123 Inf  9.067165  9.949589
##  FU2 data collected before COVID-19  9.512742 0.2285054 Inf  9.064879  9.960604
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelMAT_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##   0.3647938 0.08637129 Inf   4.224  <.0001
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.0043649 0.08613823 Inf  -0.051  0.9596
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelMAT_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL asymp.UCL
##   0.3647938 0.08637129 Inf  0.1955092 0.5340784
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL asymp.UCL
##  -0.0043649 0.08613823 Inf -0.1731927 0.1644629
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.4.3.3) Graph of estimated marginal means

MAT_lsmeans_adj10trun <- summary(lsmeans(modelMAT_adj10trun, ~Pandemic|timefactor))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.

MAT_lsmeans_adj10trun$Time<-NA
MAT_lsmeans_adj10trun$Time[MAT_lsmeans_adj10trun$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj10trun$Time[MAT_lsmeans_adj10trun$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj10trun, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status 
       (controlling for baseline)") +
  theme_bw()

16.4.4) Animal Fluency

16.4.4.1) Model

modelAnimals_adj10trun<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +
                           (1|ID), data= truncated.data_long_2)
summary(modelAnimals_adj10trun)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## Animal_Fluency_Normed ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +  
##     (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 60855.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8951 -0.5615 -0.0174  0.5379  4.2565 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.361    1.536   
##  Residual             3.190    1.786   
## Number of obs: 13639, groups:  ID, 6978
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             5.013e+00  3.358e-01
## timefactor2                                             9.184e-02  4.175e-02
## PandemicFU2 data collected before COVID-19              1.277e-01  5.799e-02
## Age                                                    -1.342e-02  3.101e-03
## SexM                                                   -1.421e-01  5.406e-02
## EducationHigh School Diploma                            1.751e-01  7.594e-02
## EducationLess than High School Diploma                  1.086e-01  1.076e-01
## EducationSome College                                   1.726e-01  9.413e-02
## EthnicityWhite                                          5.258e-01  1.507e-01
## IncomeLevel>$150k                                      -8.356e-02  1.388e-01
## IncomeLevel$100-150k                                   -2.957e-02  1.128e-01
## IncomeLevel$20-50k                                     -1.278e-01  7.593e-02
## IncomeLevel$50-100k                                    -2.310e-03  8.171e-02
## BMI                                                    -8.993e-03  5.119e-03
## CESD.10baseline                                        -1.085e-02  6.115e-03
## SmokingStatusFormer Smoker                              2.019e-01  1.034e-01
## SmokingStatusNever Smoked                               1.843e-01  1.075e-01
## SmokingStatusOccasional Smoker                          4.889e-02  2.038e-01
## RelationshipstatusMarried                               2.505e-02  8.898e-02
## RelationshipstatusSeparated                             2.000e-01  1.721e-01
## RelationshipstatusSingle                                7.335e-02  1.206e-01
## RelationshipstatusWidowed                              -7.619e-02  1.233e-01
## LivingstatusAssisted Living                            -1.263e-01  3.754e-01
## LivingstatusHouse                                       1.492e-01  8.113e-02
## LivingstatusOther                                      -2.717e-01  3.004e-01
## AnxietyYes                                              9.323e-02  1.062e-01
## MoodDisordYes                                           5.699e-02  7.666e-02
## Chronicconditions                                      -8.022e-03  1.251e-02
## PASE_TOTALbaseline                                      6.618e-04  3.594e-04
## Animal_Fluency_Normedbaseline                           5.208e-01  7.853e-03
## timefactor2:PandemicFU2 data collected before COVID-19  3.455e-02  6.171e-02
##                                                                df t value
## (Intercept)                                             6.952e+03  14.931
## timefactor2                                             6.849e+03   2.200
## PandemicFU2 data collected before COVID-19              1.145e+04   2.202
## Age                                                     6.899e+03  -4.329
## SexM                                                    6.906e+03  -2.629
## EducationHigh School Diploma                            6.918e+03   2.306
## EducationLess than High School Diploma                  6.991e+03   1.009
## EducationSome College                                   6.862e+03   1.834
## EthnicityWhite                                          6.856e+03   3.489
## IncomeLevel>$150k                                       6.915e+03  -0.602
## IncomeLevel$100-150k                                    6.884e+03  -0.262
## IncomeLevel$20-50k                                      6.909e+03  -1.684
## IncomeLevel$50-100k                                     6.909e+03  -0.028
## BMI                                                     6.889e+03  -1.757
## CESD.10baseline                                         6.924e+03  -1.774
## SmokingStatusFormer Smoker                              6.917e+03   1.953
## SmokingStatusNever Smoked                               6.916e+03   1.714
## SmokingStatusOccasional Smoker                          6.843e+03   0.240
## RelationshipstatusMarried                               6.942e+03   0.281
## RelationshipstatusSeparated                             6.966e+03   1.162
## RelationshipstatusSingle                                6.941e+03   0.608
## RelationshipstatusWidowed                               6.915e+03  -0.618
## LivingstatusAssisted Living                             6.968e+03  -0.337
## LivingstatusHouse                                       6.923e+03   1.839
## LivingstatusOther                                       6.785e+03  -0.904
## AnxietyYes                                              6.899e+03   0.878
## MoodDisordYes                                           6.916e+03   0.743
## Chronicconditions                                       6.885e+03  -0.641
## PASE_TOTALbaseline                                      6.897e+03   1.841
## Animal_Fluency_Normedbaseline                           6.893e+03  66.315
## timefactor2:PandemicFU2 data collected before COVID-19  6.787e+03   0.560
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            0.027867 *  
## PandemicFU2 data collected before COVID-19             0.027696 *  
## Age                                                    1.52e-05 ***
## SexM                                                   0.008578 ** 
## EducationHigh School Diploma                           0.021129 *  
## EducationLess than High School Diploma                 0.312993    
## EducationSome College                                  0.066762 .  
## EthnicityWhite                                         0.000488 ***
## IncomeLevel>$150k                                      0.547131    
## IncomeLevel$100-150k                                   0.793217    
## IncomeLevel$20-50k                                     0.092297 .  
## IncomeLevel$50-100k                                    0.977449    
## BMI                                                    0.078991 .  
## CESD.10baseline                                        0.076036 .  
## SmokingStatusFormer Smoker                             0.050850 .  
## SmokingStatusNever Smoked                              0.086588 .  
## SmokingStatusOccasional Smoker                         0.810470    
## RelationshipstatusMarried                              0.778341    
## RelationshipstatusSeparated                            0.245295    
## RelationshipstatusSingle                               0.543007    
## RelationshipstatusWidowed                              0.536662    
## LivingstatusAssisted Living                            0.736431    
## LivingstatusHouse                                      0.065971 .  
## LivingstatusOther                                      0.365781    
## AnxietyYes                                             0.380075    
## MoodDisordYes                                          0.457293    
## Chronicconditions                                      0.521274    
## PASE_TOTALbaseline                                     0.065616 .  
## Animal_Fluency_Normedbaseline                           < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.575518    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.4.4.2) Estimated marginal means

lsmeans(modelAnimals_adj10trun, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.814931 0.1645074 Inf  9.492503  10.13736
##  FU2 data collected before COVID-19  9.942622 0.1666697 Inf  9.615955  10.26929
## 
## timefactor = 2:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.906772 0.1645825 Inf  9.584196  10.22935
##  FU2 data collected before COVID-19 10.069016 0.1667465 Inf  9.742199  10.39583
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelAnimals_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1276904 0.05799245 Inf  -2.202  0.0277
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df z.ratio p.value
##  -0.1622442 0.05841745 Inf  -2.777  0.0055
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelAnimals_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.1276904 0.05799245 Inf -0.2413535 -0.01402729
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.1622442 0.05841745 Inf -0.2767403 -0.04774811
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.4.4.3) Graph of estimated marginal means

Animals_lsmeans_adj10trun <- summary(lsmeans(modelAnimals_adj10trun, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.

Animals_lsmeans_adj10trun$Time<-NA
Animals_lsmeans_adj10trun$Time[Animals_lsmeans_adj10trun$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj10trun$Time[Animals_lsmeans_adj10trun$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj10trun, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

16.5) Age and Sex Differences (not adjusted for baseline)

16.5.1) RVLT Immediate Recall

16.5.1.1) Model

modelRVLT_imm_11<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                         (1|ID), data= truncated.data_long)
summary(modelRVLT_imm_11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Immediate_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: truncated.data_long
## 
## REML criterion at convergence: 109081.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6731 -0.5811 -0.0495  0.5167  4.6522 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.242    2.290   
##  Residual             8.307    2.882   
## Number of obs: 20522, groups:  ID, 6987
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                8.474e+00
## timefactor2                                                                1.102e+00
## timefactor3                                                                1.510e+00
## PandemicFU2 data collected before COVID-19                                 5.873e-01
## Age_sexFemales 65+                                                         1.243e+00
## Age_sexMales 45-64                                                        -6.491e-01
## Age_sexMales 65+                                                           2.803e-01
## EducationHigh School Diploma                                               1.941e-01
## EducationLess than High School Diploma                                     6.152e-01
## EducationSome College                                                      2.454e-01
## EthnicityWhite                                                             1.236e+00
## IncomeLevel>$150k                                                          8.328e-01
## IncomeLevel$100-150k                                                       4.329e-01
## IncomeLevel$20-50k                                                         8.769e-02
## IncomeLevel$50-100k                                                        5.177e-01
## BMI                                                                       -1.915e-02
## CESD.20.1                                                                 -3.736e-02
## SmokingStatusFormer Smoker                                                 3.708e-01
## SmokingStatusNever Smoked                                                  5.042e-01
## SmokingStatusOccasional Smoker                                             4.196e-01
## RelationshipstatusMarried                                                  1.011e-01
## RelationshipstatusSeparated                                               -1.735e-01
## RelationshipstatusSingle                                                  -6.697e-02
## RelationshipstatusWidowed                                                 -4.077e-02
## LivingstatusAssisted Living                                               -3.614e-01
## LivingstatusHouse                                                          2.335e-02
## LivingstatusOther                                                         -5.312e-01
## AnxietyYes                                                                -6.354e-02
## MoodDisordYes                                                             -8.227e-02
## Chronicconditions                                                         -2.719e-02
## PASE_TOTALbaseline                                                         2.658e-03
## timefactor2:PandemicFU2 data collected before COVID-19                    -3.405e-01
## timefactor3:PandemicFU2 data collected before COVID-19                    -5.227e-01
## timefactor2:Age_sexFemales 65+                                            -1.060e+00
## timefactor3:Age_sexFemales 65+                                            -1.554e+00
## timefactor2:Age_sexMales 45-64                                            -2.459e-01
## timefactor3:Age_sexMales 45-64                                            -1.443e-01
## timefactor2:Age_sexMales 65+                                              -9.250e-01
## timefactor3:Age_sexMales 65+                                              -1.347e+00
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             -4.720e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             -2.969e-03
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               -4.434e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  4.717e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  5.717e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.395e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  3.533e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    3.618e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    7.819e-01
##                                                                           Std. Error
## (Intercept)                                                                3.742e-01
## timefactor2                                                                1.239e-01
## timefactor3                                                                1.257e-01
## PandemicFU2 data collected before COVID-19                                 1.536e-01
## Age_sexFemales 65+                                                         2.027e-01
## Age_sexMales 45-64                                                         1.484e-01
## Age_sexMales 65+                                                           1.888e-01
## EducationHigh School Diploma                                               1.077e-01
## EducationLess than High School Diploma                                     1.527e-01
## EducationSome College                                                      1.337e-01
## EthnicityWhite                                                             2.131e-01
## IncomeLevel>$150k                                                          1.969e-01
## IncomeLevel$100-150k                                                       1.599e-01
## IncomeLevel$20-50k                                                         1.080e-01
## IncomeLevel$50-100k                                                        1.161e-01
## BMI                                                                        7.253e-03
## CESD.20.1                                                                  8.651e-03
## SmokingStatusFormer Smoker                                                 1.466e-01
## SmokingStatusNever Smoked                                                  1.527e-01
## SmokingStatusOccasional Smoker                                             2.897e-01
## RelationshipstatusMarried                                                  1.262e-01
## RelationshipstatusSeparated                                                2.441e-01
## RelationshipstatusSingle                                                   1.709e-01
## RelationshipstatusWidowed                                                  1.749e-01
## LivingstatusAssisted Living                                                5.317e-01
## LivingstatusHouse                                                          1.151e-01
## LivingstatusOther                                                          4.282e-01
## AnxietyYes                                                                 1.506e-01
## MoodDisordYes                                                              1.088e-01
## Chronicconditions                                                          1.762e-02
## PASE_TOTALbaseline                                                         4.945e-04
## timefactor2:PandemicFU2 data collected before COVID-19                     1.708e-01
## timefactor3:PandemicFU2 data collected before COVID-19                     1.721e-01
## timefactor2:Age_sexFemales 65+                                             2.151e-01
## timefactor3:Age_sexFemales 65+                                             2.182e-01
## timefactor2:Age_sexMales 45-64                                             1.617e-01
## timefactor3:Age_sexMales 45-64                                             1.632e-01
## timefactor2:Age_sexMales 65+                                               2.052e-01
## timefactor3:Age_sexMales 65+                                               2.085e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.676e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              2.232e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.631e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.976e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  3.003e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.484e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.494e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.931e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.956e-01
##                                                                                   df
## (Intercept)                                                                7.423e+03
## timefactor2                                                                1.357e+04
## timefactor3                                                                1.368e+04
## PandemicFU2 data collected before COVID-19                                 1.574e+04
## Age_sexFemales 65+                                                         1.473e+04
## Age_sexMales 45-64                                                         1.526e+04
## Age_sexMales 65+                                                           1.522e+04
## EducationHigh School Diploma                                               6.929e+03
## EducationLess than High School Diploma                                     6.990e+03
## EducationSome College                                                      6.895e+03
## EthnicityWhite                                                             6.895e+03
## IncomeLevel>$150k                                                          6.931e+03
## IncomeLevel$100-150k                                                       6.906e+03
## IncomeLevel$20-50k                                                         6.929e+03
## IncomeLevel$50-100k                                                        6.928e+03
## BMI                                                                        6.912e+03
## CESD.20.1                                                                  6.919e+03
## SmokingStatusFormer Smoker                                                 6.932e+03
## SmokingStatusNever Smoked                                                  6.933e+03
## SmokingStatusOccasional Smoker                                             6.859e+03
## RelationshipstatusMarried                                                  6.932e+03
## RelationshipstatusSeparated                                                6.961e+03
## RelationshipstatusSingle                                                   6.931e+03
## RelationshipstatusWidowed                                                  6.935e+03
## LivingstatusAssisted Living                                                6.928e+03
## LivingstatusHouse                                                          6.934e+03
## LivingstatusOther                                                          6.852e+03
## AnxietyYes                                                                 6.908e+03
## MoodDisordYes                                                              6.921e+03
## Chronicconditions                                                          6.920e+03
## PASE_TOTALbaseline                                                         6.912e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.356e+04
## timefactor3:PandemicFU2 data collected before COVID-19                     1.362e+04
## timefactor2:Age_sexFemales 65+                                             1.356e+04
## timefactor3:Age_sexFemales 65+                                             1.367e+04
## timefactor2:Age_sexMales 45-64                                             1.357e+04
## timefactor3:Age_sexMales 45-64                                             1.365e+04
## timefactor2:Age_sexMales 65+                                               1.357e+04
## timefactor3:Age_sexMales 65+                                               1.369e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.573e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.577e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.577e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.357e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.364e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.356e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.359e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.357e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.363e+04
##                                                                           t value
## (Intercept)                                                                22.644
## timefactor2                                                                 8.895
## timefactor3                                                                12.017
## PandemicFU2 data collected before COVID-19                                  3.824
## Age_sexFemales 65+                                                          6.130
## Age_sexMales 45-64                                                         -4.374
## Age_sexMales 65+                                                            1.484
## EducationHigh School Diploma                                                1.803
## EducationLess than High School Diploma                                      4.030
## EducationSome College                                                       1.836
## EthnicityWhite                                                              5.801
## IncomeLevel>$150k                                                           4.229
## IncomeLevel$100-150k                                                        2.708
## IncomeLevel$20-50k                                                          0.812
## IncomeLevel$50-100k                                                         4.459
## BMI                                                                        -2.640
## CESD.20.1                                                                  -4.319
## SmokingStatusFormer Smoker                                                  2.529
## SmokingStatusNever Smoked                                                   3.301
## SmokingStatusOccasional Smoker                                              1.449
## RelationshipstatusMarried                                                   0.801
## RelationshipstatusSeparated                                                -0.711
## RelationshipstatusSingle                                                   -0.392
## RelationshipstatusWidowed                                                  -0.233
## LivingstatusAssisted Living                                                -0.680
## LivingstatusHouse                                                           0.203
## LivingstatusOther                                                          -1.241
## AnxietyYes                                                                 -0.422
## MoodDisordYes                                                              -0.756
## Chronicconditions                                                          -1.544
## PASE_TOTALbaseline                                                          5.376
## timefactor2:PandemicFU2 data collected before COVID-19                     -1.994
## timefactor3:PandemicFU2 data collected before COVID-19                     -3.038
## timefactor2:Age_sexFemales 65+                                             -4.927
## timefactor3:Age_sexFemales 65+                                             -7.122
## timefactor2:Age_sexMales 45-64                                             -1.521
## timefactor3:Age_sexMales 45-64                                             -0.884
## timefactor2:Age_sexMales 65+                                               -4.507
## timefactor3:Age_sexMales 65+                                               -6.463
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              -1.764
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              -0.013
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -1.686
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   1.585
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   1.904
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   0.561
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   1.417
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.234
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     2.645
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                                < 2e-16
## timefactor3                                                                < 2e-16
## PandemicFU2 data collected before COVID-19                                0.000132
## Age_sexFemales 65+                                                        8.99e-10
## Age_sexMales 45-64                                                        1.23e-05
## Age_sexMales 65+                                                          0.137753
## EducationHigh School Diploma                                              0.071467
## EducationLess than High School Diploma                                    5.63e-05
## EducationSome College                                                     0.066353
## EthnicityWhite                                                            6.89e-09
## IncomeLevel>$150k                                                         2.38e-05
## IncomeLevel$100-150k                                                      0.006790
## IncomeLevel$20-50k                                                        0.416758
## IncomeLevel$50-100k                                                       8.35e-06
## BMI                                                                       0.008305
## CESD.20.1                                                                 1.59e-05
## SmokingStatusFormer Smoker                                                0.011463
## SmokingStatusNever Smoked                                                 0.000967
## SmokingStatusOccasional Smoker                                            0.147515
## RelationshipstatusMarried                                                 0.423038
## RelationshipstatusSeparated                                               0.477225
## RelationshipstatusSingle                                                  0.695106
## RelationshipstatusWidowed                                                 0.815712
## LivingstatusAssisted Living                                               0.496698
## LivingstatusHouse                                                         0.839296
## LivingstatusOther                                                         0.214786
## AnxietyYes                                                                0.673105
## MoodDisordYes                                                             0.449638
## Chronicconditions                                                         0.122710
## PASE_TOTALbaseline                                                        7.87e-08
## timefactor2:PandemicFU2 data collected before COVID-19                    0.046173
## timefactor3:PandemicFU2 data collected before COVID-19                    0.002388
## timefactor2:Age_sexFemales 65+                                            8.47e-07
## timefactor3:Age_sexFemales 65+                                            1.12e-12
## timefactor2:Age_sexMales 45-64                                            0.128294
## timefactor3:Age_sexMales 45-64                                            0.376691
## timefactor2:Age_sexMales 65+                                              6.62e-06
## timefactor3:Age_sexMales 65+                                              1.06e-10
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.077715
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.989385
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.091883
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.112949
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.056986
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.574482
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.156599
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.217099
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.008177
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## timefactor3                                                               ***
## PandemicFU2 data collected before COVID-19                                ***
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                             
## EducationHigh School Diploma                                              .  
## EducationLess than High School Diploma                                    ***
## EducationSome College                                                     .  
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                         ***
## IncomeLevel$100-150k                                                      ** 
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       ** 
## CESD.20.1                                                                 ***
## SmokingStatusFormer Smoker                                                *  
## SmokingStatusNever Smoked                                                 ***
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                            
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                            
## PASE_TOTALbaseline                                                        ***
## timefactor2:PandemicFU2 data collected before COVID-19                    *  
## timefactor3:PandemicFU2 data collected before COVID-19                    ** 
## timefactor2:Age_sexFemales 65+                                            ***
## timefactor3:Age_sexFemales 65+                                            ***
## timefactor2:Age_sexMales 45-64                                               
## timefactor3:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                              ***
## timefactor3:Age_sexMales 65+                                              ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             .  
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               .  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ .  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.5.1.2) Estimated marginal means

lsmeans(modelRVLT_imm_11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.418959 0.2545590 Inf  8.920033  9.917886
##  FU2 data collected before COVID-19 10.006212 0.2539210 Inf  9.508536 10.503888
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.521273 0.2550483 Inf 10.021387 11.021158
##  FU2 data collected before COVID-19 10.767993 0.2542857 Inf 10.269602 11.266384
## 
## timefactor = 3, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.929057 0.2558349 Inf 10.427630 11.430484
##  FU2 data collected before COVID-19 10.993626 0.2542776 Inf 10.495251 11.492000
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.661829 0.2793734 Inf 10.114267 11.209391
##  FU2 data collected before COVID-19 10.777033 0.2791038 Inf 10.230000 11.324067
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.704509 0.2799432 Inf 10.155831 11.253188
##  FU2 data collected before COVID-19 10.950906 0.2801550 Inf 10.401812 11.500000
## 
## timefactor = 3, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.617637 0.2815694 Inf 10.065771 11.169503
##  FU2 data collected before COVID-19 10.781843 0.2806096 Inf 10.231859 11.331828
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   8.769881 0.2457746 Inf  8.288172  9.251590
##  FU2 data collected before COVID-19  9.354165 0.2638038 Inf  8.837119  9.871211
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.626316 0.2460711 Inf  9.144025 10.108606
##  FU2 data collected before COVID-19 10.009555 0.2642080 Inf  9.491717 10.527393
## 
## timefactor = 3, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.135684 0.2462632 Inf  9.653017 10.618351
##  FU2 data collected before COVID-19 10.550537 0.2641237 Inf 10.032864 11.068210
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.699232 0.2763024 Inf  9.157690 10.240775
##  FU2 data collected before COVID-19  9.843042 0.2783703 Inf  9.297446 10.388638
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.876585 0.2770129 Inf  9.333650 10.419520
##  FU2 data collected before COVID-19 10.041657 0.2791889 Inf  9.494457 10.588858
## 
## timefactor = 3, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.861934 0.2787644 Inf  9.315565 10.408302
##  FU2 data collected before COVID-19 10.264919 0.2793393 Inf  9.717424 10.812414
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_imm_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.5872533 0.1535869 Inf  -3.824  0.0001
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2467203 0.1548612 Inf  -1.593  0.1111
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0645682 0.1562887 Inf  -0.413  0.6795
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1152043 0.2195332 Inf  -0.525  0.5997
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2463965 0.2216559 Inf  -1.112  0.2663
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1642059 0.2243341 Inf  -0.732  0.4642
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.5842844 0.1622836 Inf  -3.600  0.0003
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3832393 0.1636138 Inf  -2.342  0.0192
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.4148529 0.1636646 Inf  -2.535  0.0113
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1438095 0.2138153 Inf  -0.673  0.5012
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1650725 0.2158388 Inf  -0.765  0.4444
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.4029855 0.2181853 Inf  -1.847  0.0647
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_imm_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.5872533 0.1535869 Inf -0.8882780 -0.28622855
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.2467203 0.1548612 Inf -0.5502427  0.05680201
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.0645682 0.1562887 Inf -0.3708884  0.24175194
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.1152043 0.2195332 Inf -0.5454815  0.31507298
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.2463965 0.2216559 Inf -0.6808341  0.18804099
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.1642059 0.2243341 Inf -0.6038926  0.27548077
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.5842844 0.1622836 Inf -0.9023545 -0.26621437
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.3832393 0.1636138 Inf -0.7039166 -0.06256208
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.4148529 0.1636646 Inf -0.7356297 -0.09407616
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.1438095 0.2138153 Inf -0.5628797  0.27526074
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.1650725 0.2158388 Inf -0.5881087  0.25796377
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.4029855 0.2181853 Inf -0.8306208  0.02464981
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.5.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_11 <- summary(lsmeans(modelRVLT_imm_11, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTimmediate_lsmeans_11$Time<-NA
RVLTimmediate_lsmeans_11$Time[RVLTimmediate_lsmeans_11$timefactor==1]<-"Baseline"
RVLTimmediate_lsmeans_11$Time[RVLTimmediate_lsmeans_11$timefactor==2]<-"Follow-up 1"
RVLTimmediate_lsmeans_11$Time[RVLTimmediate_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "RVLT Immediate Score", title = "RVLT Immediate Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

16.5.2) RVLT Delayed Recall

16.5.2.1) Model

modelRVLT_del_11<- lmer(RVLT_Delayed_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= truncated.data_long)
summary(modelRVLT_del_11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: truncated.data_long
## 
## REML criterion at convergence: 107433.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9079 -0.5669 -0.0367  0.5139  4.9297 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.595    2.365   
##  Residual             7.637    2.763   
## Number of obs: 20402, groups:  ID, 6987
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                9.039e+00
## timefactor2                                                                8.822e-01
## timefactor3                                                                1.681e+00
## PandemicFU2 data collected before COVID-19                                 4.730e-01
## Age_sexFemales 65+                                                         1.377e+00
## Age_sexMales 45-64                                                        -5.493e-01
## Age_sexMales 65+                                                           6.190e-01
## EducationHigh School Diploma                                               3.732e-01
## EducationLess than High School Diploma                                     5.817e-01
## EducationSome College                                                      3.989e-01
## EthnicityWhite                                                             1.144e+00
## IncomeLevel>$150k                                                          6.599e-01
## IncomeLevel$100-150k                                                       5.069e-01
## IncomeLevel$20-50k                                                         2.210e-01
## IncomeLevel$50-100k                                                        5.773e-01
## BMI                                                                       -1.988e-02
## CESD.20.1                                                                 -3.743e-02
## SmokingStatusFormer Smoker                                                 1.042e-01
## SmokingStatusNever Smoked                                                  3.396e-01
## SmokingStatusOccasional Smoker                                             3.041e-01
## RelationshipstatusMarried                                                 -4.634e-02
## RelationshipstatusSeparated                                               -1.580e-01
## RelationshipstatusSingle                                                  -2.299e-01
## RelationshipstatusWidowed                                                 -7.868e-02
## LivingstatusAssisted Living                                               -1.152e+00
## LivingstatusHouse                                                          2.902e-03
## LivingstatusOther                                                         -3.007e-01
## AnxietyYes                                                                -5.421e-02
## MoodDisordYes                                                             -1.110e-01
## Chronicconditions                                                         -3.616e-02
## PASE_TOTALbaseline                                                         2.737e-03
## timefactor2:PandemicFU2 data collected before COVID-19                    -1.426e-01
## timefactor3:PandemicFU2 data collected before COVID-19                    -5.683e-01
## timefactor2:Age_sexFemales 65+                                            -9.012e-01
## timefactor3:Age_sexFemales 65+                                            -1.519e+00
## timefactor2:Age_sexMales 45-64                                            -1.366e-01
## timefactor3:Age_sexMales 45-64                                            -2.205e-01
## timefactor2:Age_sexMales 65+                                              -8.688e-01
## timefactor3:Age_sexMales 65+                                              -1.455e+00
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             -4.786e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.409e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               -3.963e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  5.582e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  9.262e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  9.290e-03
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.579e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    3.406e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    7.112e-01
##                                                                           Std. Error
## (Intercept)                                                                3.774e-01
## timefactor2                                                                1.191e-01
## timefactor3                                                                1.205e-01
## PandemicFU2 data collected before COVID-19                                 1.518e-01
## Age_sexFemales 65+                                                         2.008e-01
## Age_sexMales 45-64                                                         1.468e-01
## Age_sexMales 65+                                                           1.868e-01
## EducationHigh School Diploma                                               1.087e-01
## EducationLess than High School Diploma                                     1.542e-01
## EducationSome College                                                      1.348e-01
## EthnicityWhite                                                             2.157e-01
## IncomeLevel>$150k                                                          1.986e-01
## IncomeLevel$100-150k                                                       1.613e-01
## IncomeLevel$20-50k                                                         1.090e-01
## IncomeLevel$50-100k                                                        1.171e-01
## BMI                                                                        7.315e-03
## CESD.20.1                                                                  8.732e-03
## SmokingStatusFormer Smoker                                                 1.479e-01
## SmokingStatusNever Smoked                                                  1.540e-01
## SmokingStatusOccasional Smoker                                             2.925e-01
## RelationshipstatusMarried                                                  1.274e-01
## RelationshipstatusSeparated                                                2.464e-01
## RelationshipstatusSingle                                                   1.724e-01
## RelationshipstatusWidowed                                                  1.765e-01
## LivingstatusAssisted Living                                                5.369e-01
## LivingstatusHouse                                                          1.162e-01
## LivingstatusOther                                                          4.328e-01
## AnxietyYes                                                                 1.520e-01
## MoodDisordYes                                                              1.097e-01
## Chronicconditions                                                          1.777e-02
## PASE_TOTALbaseline                                                         4.992e-04
## timefactor2:PandemicFU2 data collected before COVID-19                     1.641e-01
## timefactor3:PandemicFU2 data collected before COVID-19                     1.651e-01
## timefactor2:Age_sexFemales 65+                                             2.067e-01
## timefactor3:Age_sexFemales 65+                                             2.106e-01
## timefactor2:Age_sexMales 45-64                                             1.555e-01
## timefactor3:Age_sexMales 45-64                                             1.567e-01
## timefactor2:Age_sexMales 65+                                               1.979e-01
## timefactor3:Age_sexMales 65+                                               2.007e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.645e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              2.205e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.600e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.861e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.892e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.388e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.395e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.825e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.845e-01
##                                                                                   df
## (Intercept)                                                                7.404e+03
## timefactor2                                                                1.348e+04
## timefactor3                                                                1.356e+04
## PandemicFU2 data collected before COVID-19                                 1.501e+04
## Age_sexFemales 65+                                                         1.405e+04
## Age_sexMales 45-64                                                         1.455e+04
## Age_sexMales 65+                                                           1.452e+04
## EducationHigh School Diploma                                               6.937e+03
## EducationLess than High School Diploma                                     7.024e+03
## EducationSome College                                                      6.887e+03
## EthnicityWhite                                                             6.969e+03
## IncomeLevel>$150k                                                          6.928e+03
## IncomeLevel$100-150k                                                       6.909e+03
## IncomeLevel$20-50k                                                         6.938e+03
## IncomeLevel$50-100k                                                        6.931e+03
## BMI                                                                        6.908e+03
## CESD.20.1                                                                  6.935e+03
## SmokingStatusFormer Smoker                                                 6.924e+03
## SmokingStatusNever Smoked                                                  6.926e+03
## SmokingStatusOccasional Smoker                                             6.884e+03
## RelationshipstatusMarried                                                  6.942e+03
## RelationshipstatusSeparated                                                6.978e+03
## RelationshipstatusSingle                                                   6.931e+03
## RelationshipstatusWidowed                                                  6.946e+03
## LivingstatusAssisted Living                                                6.958e+03
## LivingstatusHouse                                                          6.949e+03
## LivingstatusOther                                                          6.900e+03
## AnxietyYes                                                                 6.916e+03
## MoodDisordYes                                                              6.916e+03
## Chronicconditions                                                          6.921e+03
## PASE_TOTALbaseline                                                         6.925e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.346e+04
## timefactor3:PandemicFU2 data collected before COVID-19                     1.351e+04
## timefactor2:Age_sexFemales 65+                                             1.346e+04
## timefactor3:Age_sexFemales 65+                                             1.359e+04
## timefactor2:Age_sexMales 45-64                                             1.348e+04
## timefactor3:Age_sexMales 45-64                                             1.353e+04
## timefactor2:Age_sexMales 65+                                               1.349e+04
## timefactor3:Age_sexMales 65+                                               1.359e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.500e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.504e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.505e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.347e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.355e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.347e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.349e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.349e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.354e+04
##                                                                           t value
## (Intercept)                                                                23.955
## timefactor2                                                                 7.405
## timefactor3                                                                13.949
## PandemicFU2 data collected before COVID-19                                  3.116
## Age_sexFemales 65+                                                          6.860
## Age_sexMales 45-64                                                         -3.741
## Age_sexMales 65+                                                            3.313
## EducationHigh School Diploma                                                3.435
## EducationLess than High School Diploma                                      3.772
## EducationSome College                                                       2.959
## EthnicityWhite                                                              5.307
## IncomeLevel>$150k                                                           3.322
## IncomeLevel$100-150k                                                        3.143
## IncomeLevel$20-50k                                                          2.028
## IncomeLevel$50-100k                                                         4.929
## BMI                                                                        -2.718
## CESD.20.1                                                                  -4.286
## SmokingStatusFormer Smoker                                                  0.705
## SmokingStatusNever Smoked                                                   2.205
## SmokingStatusOccasional Smoker                                              1.039
## RelationshipstatusMarried                                                  -0.364
## RelationshipstatusSeparated                                                -0.641
## RelationshipstatusSingle                                                   -1.334
## RelationshipstatusWidowed                                                  -0.446
## LivingstatusAssisted Living                                                -2.145
## LivingstatusHouse                                                           0.025
## LivingstatusOther                                                          -0.695
## AnxietyYes                                                                 -0.357
## MoodDisordYes                                                              -1.012
## Chronicconditions                                                          -2.034
## PASE_TOTALbaseline                                                          5.483
## timefactor2:PandemicFU2 data collected before COVID-19                     -0.869
## timefactor3:PandemicFU2 data collected before COVID-19                     -3.442
## timefactor2:Age_sexFemales 65+                                             -4.360
## timefactor3:Age_sexFemales 65+                                             -7.215
## timefactor2:Age_sexMales 45-64                                             -0.879
## timefactor3:Age_sexMales 45-64                                             -1.407
## timefactor2:Age_sexMales 65+                                               -4.391
## timefactor3:Age_sexMales 65+                                               -7.251
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              -1.810
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.639
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -1.524
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   1.951
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   3.202
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   0.039
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   1.077
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.206
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     2.500
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               1.39e-13
## timefactor3                                                                < 2e-16
## PandemicFU2 data collected before COVID-19                                0.001838
## Age_sexFemales 65+                                                        7.16e-12
## Age_sexMales 45-64                                                        0.000184
## Age_sexMales 65+                                                          0.000924
## EducationHigh School Diploma                                              0.000597
## EducationLess than High School Diploma                                    0.000163
## EducationSome College                                                     0.003094
## EthnicityWhite                                                            1.15e-07
## IncomeLevel>$150k                                                         0.000898
## IncomeLevel$100-150k                                                      0.001680
## IncomeLevel$20-50k                                                        0.042612
## IncomeLevel$50-100k                                                       8.44e-07
## BMI                                                                       0.006587
## CESD.20.1                                                                 1.84e-05
## SmokingStatusFormer Smoker                                                0.480960
## SmokingStatusNever Smoked                                                 0.027505
## SmokingStatusOccasional Smoker                                            0.298616
## RelationshipstatusMarried                                                 0.716011
## RelationshipstatusSeparated                                               0.521445
## RelationshipstatusSingle                                                  0.182289
## RelationshipstatusWidowed                                                 0.655844
## LivingstatusAssisted Living                                               0.031998
## LivingstatusHouse                                                         0.980077
## LivingstatusOther                                                         0.487184
## AnxietyYes                                                                0.721369
## MoodDisordYes                                                             0.311703
## Chronicconditions                                                         0.041940
## PASE_TOTALbaseline                                                        4.34e-08
## timefactor2:PandemicFU2 data collected before COVID-19                    0.384648
## timefactor3:PandemicFU2 data collected before COVID-19                    0.000579
## timefactor2:Age_sexFemales 65+                                            1.31e-05
## timefactor3:Age_sexFemales 65+                                            5.69e-13
## timefactor2:Age_sexMales 45-64                                            0.379674
## timefactor3:Age_sexMales 45-64                                            0.159432
## timefactor2:Age_sexMales 65+                                              1.14e-05
## timefactor3:Age_sexMales 65+                                              4.35e-13
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.070366
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.523004
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.127505
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.051052
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.001367
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.968962
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.281646
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.227871
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.012438
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## timefactor3                                                               ***
## PandemicFU2 data collected before COVID-19                                ** 
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          ***
## EducationHigh School Diploma                                              ***
## EducationLess than High School Diploma                                    ***
## EducationSome College                                                     ** 
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                         ***
## IncomeLevel$100-150k                                                      ** 
## IncomeLevel$20-50k                                                        *  
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       ** 
## CESD.20.1                                                                 ***
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                 *  
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                               *  
## LivingstatusHouse                                                            
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         *  
## PASE_TOTALbaseline                                                        ***
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor3:PandemicFU2 data collected before COVID-19                    ***
## timefactor2:Age_sexFemales 65+                                            ***
## timefactor3:Age_sexFemales 65+                                            ***
## timefactor2:Age_sexMales 45-64                                               
## timefactor3:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                              ***
## timefactor3:Age_sexMales 65+                                              ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             .  
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ .  
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ ** 
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.5.2.2) Estimated marginal means

lsmeans(modelRVLT_del_11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.644094 0.2561104 Inf  9.142126 10.146061
##  FU2 data collected before COVID-19 10.117081 0.2555687 Inf  9.616175 10.617986
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.526324 0.2567855 Inf 10.023034 11.029614
##  FU2 data collected before COVID-19 10.856667 0.2559289 Inf 10.355055 11.358278
## 
## timefactor = 3, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.325388 0.2572887 Inf 10.821112 11.829665
##  FU2 data collected before COVID-19 11.230056 0.2559690 Inf 10.728366 11.731746
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.021320 0.2802864 Inf 10.471969 11.570671
##  FU2 data collected before COVID-19 11.015718 0.2801789 Inf 10.466577 11.564858
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.002332 0.2809530 Inf 10.451675 11.552990
##  FU2 data collected before COVID-19 11.412239 0.2814886 Inf 10.860532 11.963947
## 
## timefactor = 3, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.183545 0.2834169 Inf 10.628058 11.739032
##  FU2 data collected before COVID-19 11.535817 0.2819040 Inf 10.983295 12.088338
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.094823 0.2475212 Inf  8.609690  9.579955
##  FU2 data collected before COVID-19  9.708679 0.2650545 Inf  9.189181 10.228176
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.840437 0.2480015 Inf  9.354363 10.326511
##  FU2 data collected before COVID-19 10.320939 0.2655865 Inf  9.800399 10.841479
## 
## timefactor = 3, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.555664 0.2480501 Inf 10.069494 11.041833
##  FU2 data collected before COVID-19 10.859056 0.2655800 Inf 10.338529 11.379583
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.263078 0.2774136 Inf  9.719357 10.806798
##  FU2 data collected before COVID-19 10.339809 0.2793223 Inf  9.792348 10.887271
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.276555 0.2787166 Inf  9.730281 10.822830
##  FU2 data collected before COVID-19 10.551289 0.2808669 Inf 10.000800 11.101778
## 
## timefactor = 3, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.488939 0.2802947 Inf  9.939571 11.038306
##  FU2 data collected before COVID-19 10.708540 0.2806653 Inf 10.158446 11.258634
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_del_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.4729871 0.1518003 Inf  -3.116  0.0018
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3303426 0.1533256 Inf  -2.155  0.0312
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   0.0953322 0.1544562 Inf   0.617  0.5371
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   0.0056028 0.2170057 Inf   0.026  0.9794
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.4099067 0.2196553 Inf  -1.866  0.0620
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3522712 0.2230039 Inf  -1.580  0.1142
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.6138560 0.1603956 Inf  -3.827  0.0001
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.4805016 0.1621041 Inf  -2.964  0.0030
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3033924 0.1621468 Inf  -1.871  0.0613
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0767316 0.2113181 Inf  -0.363  0.7165
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2747335 0.2148483 Inf  -1.279  0.2010
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2196013 0.2166938 Inf  -1.013  0.3109
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_del_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.4729871 0.1518003 Inf -0.7705103 -0.1754639
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.3303426 0.1533256 Inf -0.6308553 -0.0298298
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##   0.0953322 0.1544562 Inf -0.2073964  0.3980607
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##   0.0056028 0.2170057 Inf -0.4197205  0.4309261
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.4099067 0.2196553 Inf -0.8404232  0.0206098
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.3522712 0.2230039 Inf -0.7893508  0.0848085
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.6138560 0.1603956 Inf -0.9282256 -0.2994863
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.4805016 0.1621041 Inf -0.7982198 -0.1627833
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.3033924 0.1621468 Inf -0.6211943  0.0144094
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.0767316 0.2113181 Inf -0.4909074  0.3374441
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.2747335 0.2148483 Inf -0.6958284  0.1463614
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.2196013 0.2166938 Inf -0.6443133  0.2051108
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.5.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_11 <- summary(lsmeans(modelRVLT_del_11, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTdelayed_lsmeans_11$Time<-NA
RVLTdelayed_lsmeans_11$Time[RVLTdelayed_lsmeans_11$timefactor==1]<-"Baseline"
RVLTdelayed_lsmeans_11$Time[RVLTdelayed_lsmeans_11$timefactor==2]<-"Follow-up 1"
RVLTdelayed_lsmeans_11$Time[RVLTdelayed_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "RVLT Delayed Score", title = "RVLT Delayed Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

16.5.3) Mental Alteration Test

16.5.3.1) Model

modelMAT_11<- lmer(MAT_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                    (1|ID), data= truncated.data_long)
summary(modelMAT_11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## MAT_Normed ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +  
##     IncomeLevel + BMI + CESD.20.1 + SmokingStatus + Relationshipstatus +  
##     Livingstatus + Anxiety + MoodDisord + Chronicconditions +  
##     PASE_TOTALbaseline + (1 | ID)
##    Data: truncated.data_long
## 
## REML criterion at convergence: 102813.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9402 -0.4850  0.0013  0.4499  4.4706 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 5.054    2.248   
##  Residual             7.614    2.759   
## Number of obs: 19603, groups:  ID, 6987
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                9.338e+00
## timefactor2                                                                2.951e+00
## timefactor3                                                               -2.984e-01
## PandemicFU2 data collected before COVID-19                                 3.757e-02
## Age_sexFemales 65+                                                         2.382e-01
## Age_sexMales 45-64                                                        -4.012e-01
## Age_sexMales 65+                                                          -5.418e-01
## EducationHigh School Diploma                                              -7.027e-03
## EducationLess than High School Diploma                                    -4.984e-02
## EducationSome College                                                      6.346e-02
## EthnicityWhite                                                             1.352e+00
## IncomeLevel>$150k                                                          7.361e-01
## IncomeLevel$100-150k                                                       8.394e-01
## IncomeLevel$20-50k                                                         3.332e-01
## IncomeLevel$50-100k                                                        6.743e-01
## BMI                                                                       -2.222e-02
## CESD.20.1                                                                 -4.779e-02
## SmokingStatusFormer Smoker                                                 1.958e-01
## SmokingStatusNever Smoked                                                  1.419e-01
## SmokingStatusOccasional Smoker                                             1.829e-01
## RelationshipstatusMarried                                                  1.626e-01
## RelationshipstatusSeparated                                               -1.999e-01
## RelationshipstatusSingle                                                   2.736e-01
## RelationshipstatusWidowed                                                 -2.039e-01
## LivingstatusAssisted Living                                               -5.060e-01
## LivingstatusHouse                                                         -1.712e-01
## LivingstatusOther                                                         -3.122e-01
## AnxietyYes                                                                 1.055e-01
## MoodDisordYes                                                              3.021e-01
## Chronicconditions                                                         -6.112e-02
## PASE_TOTALbaseline                                                        -8.248e-04
## timefactor2:PandemicFU2 data collected before COVID-19                    -1.278e+00
## timefactor3:PandemicFU2 data collected before COVID-19                     1.399e-01
## timefactor2:Age_sexFemales 65+                                            -7.824e-01
## timefactor3:Age_sexFemales 65+                                            -2.864e-01
## timefactor2:Age_sexMales 45-64                                            -3.089e+00
## timefactor3:Age_sexMales 45-64                                             2.023e-01
## timefactor2:Age_sexMales 65+                                              -3.042e+00
## timefactor3:Age_sexMales 65+                                              -1.597e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             -2.496e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.077e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                3.650e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.816e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  3.125e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.247e+00
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -2.293e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.338e+00
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.615e-02
##                                                                           Std. Error
## (Intercept)                                                                3.681e-01
## timefactor2                                                                1.207e-01
## timefactor3                                                                1.217e-01
## PandemicFU2 data collected before COVID-19                                 1.485e-01
## Age_sexFemales 65+                                                         1.964e-01
## Age_sexMales 45-64                                                         1.436e-01
## Age_sexMales 65+                                                           1.828e-01
## EducationHigh School Diploma                                               1.060e-01
## EducationLess than High School Diploma                                     1.511e-01
## EducationSome College                                                      1.312e-01
## EthnicityWhite                                                             2.105e-01
## IncomeLevel>$150k                                                          1.935e-01
## IncomeLevel$100-150k                                                       1.571e-01
## IncomeLevel$20-50k                                                         1.063e-01
## IncomeLevel$50-100k                                                        1.143e-01
## BMI                                                                        7.124e-03
## CESD.20.1                                                                  8.510e-03
## SmokingStatusFormer Smoker                                                 1.440e-01
## SmokingStatusNever Smoked                                                  1.499e-01
## SmokingStatusOccasional Smoker                                             2.848e-01
## RelationshipstatusMarried                                                  1.242e-01
## RelationshipstatusSeparated                                                2.396e-01
## RelationshipstatusSingle                                                   1.681e-01
## RelationshipstatusWidowed                                                  1.725e-01
## LivingstatusAssisted Living                                                5.237e-01
## LivingstatusHouse                                                          1.134e-01
## LivingstatusOther                                                          4.231e-01
## AnxietyYes                                                                 1.480e-01
## MoodDisordYes                                                              1.068e-01
## Chronicconditions                                                          1.734e-02
## PASE_TOTALbaseline                                                         4.859e-04
## timefactor2:PandemicFU2 data collected before COVID-19                     1.664e-01
## timefactor3:PandemicFU2 data collected before COVID-19                     1.665e-01
## timefactor2:Age_sexFemales 65+                                             2.132e-01
## timefactor3:Age_sexFemales 65+                                             2.152e-01
## timefactor2:Age_sexMales 45-64                                             1.578e-01
## timefactor3:Age_sexMales 45-64                                             1.583e-01
## timefactor2:Age_sexMales 65+                                               2.031e-01
## timefactor3:Age_sexMales 65+                                               2.070e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.588e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              2.158e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.544e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.940e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.949e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.432e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.419e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.901e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.914e-01
##                                                                                   df
## (Intercept)                                                                7.372e+03
## timefactor2                                                                1.279e+04
## timefactor3                                                                1.285e+04
## PandemicFU2 data collected before COVID-19                                 1.498e+04
## Age_sexFemales 65+                                                         1.405e+04
## Age_sexMales 45-64                                                         1.454e+04
## Age_sexMales 65+                                                           1.450e+04
## EducationHigh School Diploma                                               6.892e+03
## EducationLess than High School Diploma                                     7.068e+03
## EducationSome College                                                      6.815e+03
## EthnicityWhite                                                             6.948e+03
## IncomeLevel>$150k                                                          6.863e+03
## IncomeLevel$100-150k                                                       6.841e+03
## IncomeLevel$20-50k                                                         6.911e+03
## IncomeLevel$50-100k                                                        6.892e+03
## BMI                                                                        6.836e+03
## CESD.20.1                                                                  6.870e+03
## SmokingStatusFormer Smoker                                                 6.838e+03
## SmokingStatusNever Smoked                                                  6.839e+03
## SmokingStatusOccasional Smoker                                             6.794e+03
## RelationshipstatusMarried                                                  6.909e+03
## RelationshipstatusSeparated                                                6.875e+03
## RelationshipstatusSingle                                                   6.891e+03
## RelationshipstatusWidowed                                                  6.952e+03
## LivingstatusAssisted Living                                                6.892e+03
## LivingstatusHouse                                                          6.925e+03
## LivingstatusOther                                                          6.909e+03
## AnxietyYes                                                                 6.842e+03
## MoodDisordYes                                                              6.836e+03
## Chronicconditions                                                          6.892e+03
## PASE_TOTALbaseline                                                         6.841e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.279e+04
## timefactor3:PandemicFU2 data collected before COVID-19                     1.279e+04
## timefactor2:Age_sexFemales 65+                                             1.293e+04
## timefactor3:Age_sexFemales 65+                                             1.299e+04
## timefactor2:Age_sexMales 45-64                                             1.281e+04
## timefactor3:Age_sexMales 45-64                                             1.284e+04
## timefactor2:Age_sexMales 65+                                               1.295e+04
## timefactor3:Age_sexMales 65+                                               1.308e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.497e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.501e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.501e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.291e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.293e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.282e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.278e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.294e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.297e+04
##                                                                           t value
## (Intercept)                                                                25.370
## timefactor2                                                                24.446
## timefactor3                                                                -2.453
## PandemicFU2 data collected before COVID-19                                  0.253
## Age_sexFemales 65+                                                          1.213
## Age_sexMales 45-64                                                         -2.794
## Age_sexMales 65+                                                           -2.965
## EducationHigh School Diploma                                               -0.066
## EducationLess than High School Diploma                                     -0.330
## EducationSome College                                                       0.484
## EthnicityWhite                                                              6.422
## IncomeLevel>$150k                                                           3.803
## IncomeLevel$100-150k                                                        5.344
## IncomeLevel$20-50k                                                          3.133
## IncomeLevel$50-100k                                                         5.902
## BMI                                                                        -3.118
## CESD.20.1                                                                  -5.615
## SmokingStatusFormer Smoker                                                  1.360
## SmokingStatusNever Smoked                                                   0.946
## SmokingStatusOccasional Smoker                                              0.642
## RelationshipstatusMarried                                                   1.309
## RelationshipstatusSeparated                                                -0.834
## RelationshipstatusSingle                                                    1.628
## RelationshipstatusWidowed                                                  -1.182
## LivingstatusAssisted Living                                                -0.966
## LivingstatusHouse                                                          -1.509
## LivingstatusOther                                                          -0.738
## AnxietyYes                                                                  0.713
## MoodDisordYes                                                               2.827
## Chronicconditions                                                          -3.524
## PASE_TOTALbaseline                                                         -1.698
## timefactor2:PandemicFU2 data collected before COVID-19                     -7.680
## timefactor3:PandemicFU2 data collected before COVID-19                      0.840
## timefactor2:Age_sexFemales 65+                                             -3.671
## timefactor3:Age_sexFemales 65+                                             -1.331
## timefactor2:Age_sexMales 45-64                                            -19.582
## timefactor3:Age_sexMales 45-64                                              1.278
## timefactor2:Age_sexMales 65+                                              -14.982
## timefactor3:Age_sexMales 65+                                               -0.771
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              -0.965
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.499
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 1.435
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.618
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   1.060
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   5.128
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -0.948
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     4.614
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     0.055
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                                < 2e-16
## timefactor3                                                               0.014183
## PandemicFU2 data collected before COVID-19                                0.800289
## Age_sexFemales 65+                                                        0.225235
## Age_sexMales 45-64                                                        0.005215
## Age_sexMales 65+                                                          0.003035
## EducationHigh School Diploma                                              0.947137
## EducationLess than High School Diploma                                    0.741547
## EducationSome College                                                     0.628671
## EthnicityWhite                                                            1.43e-10
## IncomeLevel>$150k                                                         0.000144
## IncomeLevel$100-150k                                                      9.36e-08
## IncomeLevel$20-50k                                                        0.001739
## IncomeLevel$50-100k                                                       3.76e-09
## BMI                                                                       0.001827
## CESD.20.1                                                                 2.04e-08
## SmokingStatusFormer Smoker                                                0.173766
## SmokingStatusNever Smoked                                                 0.344043
## SmokingStatusOccasional Smoker                                            0.520812
## RelationshipstatusMarried                                                 0.190685
## RelationshipstatusSeparated                                               0.404098
## RelationshipstatusSingle                                                  0.103626
## RelationshipstatusWidowed                                                 0.237253
## LivingstatusAssisted Living                                               0.334010
## LivingstatusHouse                                                         0.131231
## LivingstatusOther                                                         0.460635
## AnxietyYes                                                                0.476064
## MoodDisordYes                                                             0.004709
## Chronicconditions                                                         0.000428
## PASE_TOTALbaseline                                                        0.089647
## timefactor2:PandemicFU2 data collected before COVID-19                    1.70e-14
## timefactor3:PandemicFU2 data collected before COVID-19                    0.400722
## timefactor2:Age_sexFemales 65+                                            0.000243
## timefactor3:Age_sexFemales 65+                                            0.183254
## timefactor2:Age_sexMales 45-64                                             < 2e-16
## timefactor3:Age_sexMales 45-64                                            0.201367
## timefactor2:Age_sexMales 65+                                               < 2e-16
## timefactor3:Age_sexMales 65+                                              0.440547
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.334782
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.617756
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.151315
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.536820
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.289213
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.97e-07
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.343270
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   3.99e-06
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.955809
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## timefactor3                                                               *  
## PandemicFU2 data collected before COVID-19                                   
## Age_sexFemales 65+                                                           
## Age_sexMales 45-64                                                        ** 
## Age_sexMales 65+                                                          ** 
## EducationHigh School Diploma                                                 
## EducationLess than High School Diploma                                       
## EducationSome College                                                        
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                         ***
## IncomeLevel$100-150k                                                      ***
## IncomeLevel$20-50k                                                        ** 
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       ** 
## CESD.20.1                                                                 ***
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                            
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                             ** 
## Chronicconditions                                                         ***
## PASE_TOTALbaseline                                                        .  
## timefactor2:PandemicFU2 data collected before COVID-19                    ***
## timefactor3:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                            ***
## timefactor3:Age_sexFemales 65+                                               
## timefactor2:Age_sexMales 45-64                                            ***
## timefactor3:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                              ***
## timefactor3:Age_sexMales 65+                                                 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 ***
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   ***
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.5.3.2) Estimated marginal means

lsmeans(modelMAT_11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.486104 0.2500419 Inf  8.996031  9.976177
##  FU2 data collected before COVID-19  9.523677 0.2494929 Inf  9.034680 10.012674
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  12.436735 0.2516303 Inf 11.943549 12.929921
##  FU2 data collected before COVID-19 11.196007 0.2507834 Inf 10.704480 11.687533
## 
## timefactor = 3, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.187691 0.2518532 Inf  8.694067  9.681314
##  FU2 data collected before COVID-19  9.365190 0.2503619 Inf  8.874489  9.855890
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.724277 0.2738024 Inf  9.187635 10.260920
##  FU2 data collected before COVID-19  9.512264 0.2736716 Inf  8.975877 10.048650
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.892462 0.2788904 Inf 11.345847 12.439077
##  FU2 data collected before COVID-19 10.583713 0.2776223 Inf 10.039583 11.127843
## 
## timefactor = 3, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.139463 0.2794253 Inf  8.591799  9.687126
##  FU2 data collected before COVID-19  9.379889 0.2774931 Inf  8.836012  9.923765
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.084873 0.2416041 Inf  8.611338  9.558408
##  FU2 data collected before COVID-19  9.230134 0.2588066 Inf  8.722882  9.737385
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   8.946023 0.2429148 Inf  8.469919  9.422127
##  FU2 data collected before COVID-19  9.060055 0.2614040 Inf  8.547713  9.572398
## 
## timefactor = 3, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   8.988778 0.2426544 Inf  8.513184  9.464372
##  FU2 data collected before COVID-19  9.044675 0.2603743 Inf  8.534350  9.554999
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   8.944304 0.2709466 Inf  8.413258  9.475350
##  FU2 data collected before COVID-19  9.346916 0.2728221 Inf  8.812195  9.881638
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   8.852732 0.2758962 Inf  8.311985  9.393479
##  FU2 data collected before COVID-19  9.315246 0.2778603 Inf  8.770650  9.859842
## 
## timefactor = 3, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   8.486221 0.2780339 Inf  7.941284  9.031157
##  FU2 data collected before COVID-19  9.044905 0.2768707 Inf  8.502248  9.587562
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelMAT_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0375734 0.1485249 Inf  -0.253  0.8003
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   1.2407284 0.1529348 Inf   8.113  <.0001
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1774989 0.1530202 Inf  -1.160  0.2461
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   0.2120136 0.2123251 Inf   0.999  0.3180
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   1.3087492 0.2241153 Inf   5.840  <.0001
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2404263 0.2251556 Inf  -1.068  0.2856
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1452606 0.1569348 Inf  -0.926  0.3546
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1140322 0.1631734 Inf  -0.699  0.4847
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0558966 0.1612160 Inf  -0.347  0.7288
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.4026124 0.2067627 Inf  -1.947  0.0515
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.4625139 0.2189963 Inf  -2.112  0.0347
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.5586843 0.2206571 Inf  -2.532  0.0113
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelMAT_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.0375734 0.1485249 Inf -0.3286770  0.2535301
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##   1.2407284 0.1529348 Inf  0.9409817  1.5404750
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1774989 0.1530202 Inf -0.4774131  0.1224153
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##   0.2120136 0.2123251 Inf -0.2041358  0.6281631
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##   1.3087492 0.2241153 Inf  0.8694913  1.7480071
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.2404263 0.2251556 Inf -0.6817231  0.2008705
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1452606 0.1569348 Inf -0.4528471  0.1623259
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.1140322 0.1631734 Inf -0.4338462  0.2057818
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.0558966 0.1612160 Inf -0.3718741  0.2600810
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.4026124 0.2067627 Inf -0.8078598  0.0026349
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.4625139 0.2189963 Inf -0.8917387 -0.0332891
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL  asymp.UCL
##  -0.5586843 0.2206571 Inf -0.9911642 -0.1262044
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.5.3.3) Graph of estimated marginal means

MAT_lsmeans_11 <- summary(lsmeans(modelMAT_11, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.

MAT_lsmeans_11$Time<-NA
MAT_lsmeans_11$Time[MAT_lsmeans_11$timefactor==1]<-"Baseline"
MAT_lsmeans_11$Time[MAT_lsmeans_11$timefactor==2]<-"Follow-up 1"
MAT_lsmeans_11$Time[MAT_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(MAT_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "MAT Score", title = "Mental Alteration Test Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

16.5.4) Animal Fluency

16.5.4.1) Model

modelAnimals_11<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= truncated.data_long)
summary(modelAnimals_11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Animal_Fluency_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: truncated.data_long
## 
## REML criterion at convergence: 96426.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.3008 -0.5637 -0.0115  0.5550  4.3819 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.873    2.207   
##  Residual             3.634    1.906   
## Number of obs: 20626, groups:  ID, 6987
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                8.762e+00
## timefactor2                                                               -5.881e-02
## timefactor3                                                                2.526e-01
## PandemicFU2 data collected before COVID-19                                 2.930e-01
## Age_sexFemales 65+                                                         2.547e-01
## Age_sexMales 45-64                                                        -5.258e-02
## Age_sexMales 65+                                                          -1.177e-01
## EducationHigh School Diploma                                               3.350e-01
## EducationLess than High School Diploma                                     3.501e-01
## EducationSome College                                                      2.938e-01
## EthnicityWhite                                                             1.383e+00
## IncomeLevel>$150k                                                          5.116e-01
## IncomeLevel$100-150k                                                       4.084e-01
## IncomeLevel$20-50k                                                         6.383e-02
## IncomeLevel$50-100k                                                        2.534e-01
## BMI                                                                       -1.789e-02
## CESD.20.1                                                                 -3.612e-02
## SmokingStatusFormer Smoker                                                 3.165e-01
## SmokingStatusNever Smoked                                                  2.612e-01
## SmokingStatusOccasional Smoker                                             4.441e-01
## RelationshipstatusMarried                                                 -2.526e-01
## RelationshipstatusSeparated                                               -9.152e-02
## RelationshipstatusSingle                                                  -1.653e-01
## RelationshipstatusWidowed                                                 -2.627e-01
## LivingstatusAssisted Living                                               -2.214e-01
## LivingstatusHouse                                                          3.471e-01
## LivingstatusOther                                                          2.311e-01
## AnxietyYes                                                                -5.049e-02
## MoodDisordYes                                                              2.472e-01
## Chronicconditions                                                         -1.836e-03
## PASE_TOTALbaseline                                                         9.430e-04
## timefactor2:PandemicFU2 data collected before COVID-19                     8.401e-02
## timefactor3:PandemicFU2 data collected before COVID-19                    -4.249e-02
## timefactor2:Age_sexFemales 65+                                            -3.629e-01
## timefactor3:Age_sexFemales 65+                                            -7.586e-01
## timefactor2:Age_sexMales 45-64                                            -2.952e-02
## timefactor3:Age_sexMales 45-64                                            -2.190e-01
## timefactor2:Age_sexMales 65+                                              -1.242e-01
## timefactor3:Age_sexMales 65+                                              -6.605e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             -1.301e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             -1.133e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               -1.568e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  8.826e-02
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.500e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -2.384e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.066e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   -1.079e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.680e-01
##                                                                           Std. Error
## (Intercept)                                                                3.230e-01
## timefactor2                                                                8.197e-02
## timefactor3                                                                8.299e-02
## PandemicFU2 data collected before COVID-19                                 1.218e-01
## Age_sexFemales 65+                                                         1.622e-01
## Age_sexMales 45-64                                                         1.182e-01
## Age_sexMales 65+                                                           1.504e-01
## EducationHigh School Diploma                                               9.361e-02
## EducationLess than High School Diploma                                     1.325e-01
## EducationSome College                                                      1.162e-01
## EthnicityWhite                                                             1.853e-01
## IncomeLevel>$150k                                                          1.711e-01
## IncomeLevel$100-150k                                                       1.390e-01
## IncomeLevel$20-50k                                                         9.383e-02
## IncomeLevel$50-100k                                                        1.009e-01
## BMI                                                                        6.305e-03
## CESD.20.1                                                                  7.522e-03
## SmokingStatusFormer Smoker                                                 1.274e-01
## SmokingStatusNever Smoked                                                  1.327e-01
## SmokingStatusOccasional Smoker                                             2.522e-01
## RelationshipstatusMarried                                                  1.097e-01
## RelationshipstatusSeparated                                                2.120e-01
## RelationshipstatusSingle                                                   1.485e-01
## RelationshipstatusWidowed                                                  1.520e-01
## LivingstatusAssisted Living                                                4.624e-01
## LivingstatusHouse                                                          1.001e-01
## LivingstatusOther                                                          3.725e-01
## AnxietyYes                                                                 1.310e-01
## MoodDisordYes                                                              9.460e-02
## Chronicconditions                                                          1.531e-02
## PASE_TOTALbaseline                                                         4.300e-04
## timefactor2:PandemicFU2 data collected before COVID-19                     1.128e-01
## timefactor3:PandemicFU2 data collected before COVID-19                     1.136e-01
## timefactor2:Age_sexFemales 65+                                             1.419e-01
## timefactor3:Age_sexFemales 65+                                             1.442e-01
## timefactor2:Age_sexMales 45-64                                             1.069e-01
## timefactor3:Age_sexMales 45-64                                             1.079e-01
## timefactor2:Age_sexMales 65+                                               1.354e-01
## timefactor3:Age_sexMales 65+                                               1.376e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.122e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.769e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.085e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.961e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.980e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.642e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.648e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.933e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.951e-01
##                                                                                   df
## (Intercept)                                                                7.245e+03
## timefactor2                                                                1.367e+04
## timefactor3                                                                1.373e+04
## PandemicFU2 data collected before COVID-19                                 1.245e+04
## Age_sexFemales 65+                                                         1.171e+04
## Age_sexMales 45-64                                                         1.209e+04
## Age_sexMales 65+                                                           1.206e+04
## EducationHigh School Diploma                                               6.959e+03
## EducationLess than High School Diploma                                     6.979e+03
## EducationSome College                                                      6.933e+03
## EthnicityWhite                                                             6.939e+03
## IncomeLevel>$150k                                                          6.951e+03
## IncomeLevel$100-150k                                                       6.944e+03
## IncomeLevel$20-50k                                                         6.950e+03
## IncomeLevel$50-100k                                                        6.951e+03
## BMI                                                                        6.941e+03
## CESD.20.1                                                                  6.954e+03
## SmokingStatusFormer Smoker                                                 6.951e+03
## SmokingStatusNever Smoked                                                  6.951e+03
## SmokingStatusOccasional Smoker                                             6.920e+03
## RelationshipstatusMarried                                                  6.966e+03
## RelationshipstatusSeparated                                                6.970e+03
## RelationshipstatusSingle                                                   6.962e+03
## RelationshipstatusWidowed                                                  6.951e+03
## LivingstatusAssisted Living                                                6.970e+03
## LivingstatusHouse                                                          6.960e+03
## LivingstatusOther                                                          6.898e+03
## AnxietyYes                                                                 6.946e+03
## MoodDisordYes                                                              6.953e+03
## Chronicconditions                                                          6.943e+03
## PASE_TOTALbaseline                                                         6.945e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.366e+04
## timefactor3:PandemicFU2 data collected before COVID-19                     1.369e+04
## timefactor2:Age_sexFemales 65+                                             1.365e+04
## timefactor3:Age_sexFemales 65+                                             1.372e+04
## timefactor2:Age_sexMales 45-64                                             1.366e+04
## timefactor3:Age_sexMales 45-64                                             1.371e+04
## timefactor2:Age_sexMales 65+                                               1.365e+04
## timefactor3:Age_sexMales 65+                                               1.373e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.244e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.247e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.247e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.364e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.369e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.366e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.367e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.365e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.369e+04
##                                                                           t value
## (Intercept)                                                                27.128
## timefactor2                                                                -0.717
## timefactor3                                                                 3.044
## PandemicFU2 data collected before COVID-19                                  2.406
## Age_sexFemales 65+                                                          1.571
## Age_sexMales 45-64                                                         -0.445
## Age_sexMales 65+                                                           -0.783
## EducationHigh School Diploma                                                3.579
## EducationLess than High School Diploma                                      2.643
## EducationSome College                                                       2.528
## EthnicityWhite                                                              7.463
## IncomeLevel>$150k                                                           2.990
## IncomeLevel$100-150k                                                        2.938
## IncomeLevel$20-50k                                                          0.680
## IncomeLevel$50-100k                                                         2.512
## BMI                                                                        -2.837
## CESD.20.1                                                                  -4.802
## SmokingStatusFormer Smoker                                                  2.484
## SmokingStatusNever Smoked                                                   1.969
## SmokingStatusOccasional Smoker                                              1.761
## RelationshipstatusMarried                                                  -2.302
## RelationshipstatusSeparated                                                -0.432
## RelationshipstatusSingle                                                   -1.113
## RelationshipstatusWidowed                                                  -1.729
## LivingstatusAssisted Living                                                -0.479
## LivingstatusHouse                                                           3.470
## LivingstatusOther                                                           0.620
## AnxietyYes                                                                 -0.385
## MoodDisordYes                                                               2.613
## Chronicconditions                                                          -0.120
## PASE_TOTALbaseline                                                          2.193
## timefactor2:PandemicFU2 data collected before COVID-19                      0.744
## timefactor3:PandemicFU2 data collected before COVID-19                     -0.374
## timefactor2:Age_sexFemales 65+                                             -2.557
## timefactor3:Age_sexFemales 65+                                             -5.262
## timefactor2:Age_sexMales 45-64                                             -0.276
## timefactor3:Age_sexMales 45-64                                             -2.029
## timefactor2:Age_sexMales 65+                                               -0.917
## timefactor3:Age_sexMales 65+                                               -4.799
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              -0.613
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              -0.640
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -0.752
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.450
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   1.263
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -1.452
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   0.065
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    -0.558
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.374
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               0.473078
## timefactor3                                                               0.002342
## PandemicFU2 data collected before COVID-19                                0.016122
## Age_sexFemales 65+                                                        0.116272
## Age_sexMales 45-64                                                        0.656373
## Age_sexMales 65+                                                          0.433794
## EducationHigh School Diploma                                              0.000348
## EducationLess than High School Diploma                                    0.008244
## EducationSome College                                                     0.011483
## EthnicityWhite                                                            9.46e-14
## IncomeLevel>$150k                                                         0.002804
## IncomeLevel$100-150k                                                      0.003315
## IncomeLevel$20-50k                                                        0.496393
## IncomeLevel$50-100k                                                       0.012029
## BMI                                                                       0.004563
## CESD.20.1                                                                 1.60e-06
## SmokingStatusFormer Smoker                                                0.013027
## SmokingStatusNever Smoked                                                 0.049050
## SmokingStatusOccasional Smoker                                            0.078294
## RelationshipstatusMarried                                                 0.021349
## RelationshipstatusSeparated                                               0.665962
## RelationshipstatusSingle                                                  0.265836
## RelationshipstatusWidowed                                                 0.083880
## LivingstatusAssisted Living                                               0.632181
## LivingstatusHouse                                                         0.000524
## LivingstatusOther                                                         0.535037
## AnxietyYes                                                                0.699898
## MoodDisordYes                                                             0.009005
## Chronicconditions                                                         0.904546
## PASE_TOTALbaseline                                                        0.028331
## timefactor2:PandemicFU2 data collected before COVID-19                    0.456590
## timefactor3:PandemicFU2 data collected before COVID-19                    0.708424
## timefactor2:Age_sexFemales 65+                                            0.010581
## timefactor3:Age_sexFemales 65+                                            1.45e-07
## timefactor2:Age_sexMales 45-64                                            0.782392
## timefactor3:Age_sexMales 45-64                                            0.042476
## timefactor2:Age_sexMales 65+                                              0.359024
## timefactor3:Age_sexMales 65+                                              1.61e-06
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.539768
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.521989
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.452179
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.652646
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.206708
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.146464
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.948430
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.576655
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.169546
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                                  
## timefactor3                                                               ** 
## PandemicFU2 data collected before COVID-19                                *  
## Age_sexFemales 65+                                                           
## Age_sexMales 45-64                                                           
## Age_sexMales 65+                                                             
## EducationHigh School Diploma                                              ***
## EducationLess than High School Diploma                                    ** 
## EducationSome College                                                     *  
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                         ** 
## IncomeLevel$100-150k                                                      ** 
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       *  
## BMI                                                                       ** 
## CESD.20.1                                                                 ***
## SmokingStatusFormer Smoker                                                *  
## SmokingStatusNever Smoked                                                 *  
## SmokingStatusOccasional Smoker                                            .  
## RelationshipstatusMarried                                                 *  
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                 .  
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         ***
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                             ** 
## Chronicconditions                                                            
## PASE_TOTALbaseline                                                        *  
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor3:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                            *  
## timefactor3:Age_sexFemales 65+                                            ***
## timefactor2:Age_sexMales 45-64                                               
## timefactor3:Age_sexMales 45-64                                            *  
## timefactor2:Age_sexMales 65+                                                 
## timefactor3:Age_sexMales 65+                                              ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.5.4.2) Estimated marginal means

Significant differences for females 45-64 and 65+ and significant differences males 65+

lsmeans(modelAnimals_11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.732812 0.2178217 Inf  9.305889 10.159734
##  FU2 data collected before COVID-19 10.025847 0.2176155 Inf  9.599329 10.452366
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.674000 0.2180696 Inf  9.246591 10.101409
##  FU2 data collected before COVID-19 10.051050 0.2177402 Inf  9.624287 10.477812
## 
## timefactor = 3, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.985407 0.2184097 Inf  9.557331 10.413482
##  FU2 data collected before COVID-19 10.235955 0.2177660 Inf  9.809141 10.662768
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.987538 0.2363308 Inf  9.524338 10.450738
##  FU2 data collected before COVID-19 10.150483 0.2366661 Inf  9.686626 10.614340
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.565831 0.2364584 Inf  9.102381 10.029281
##  FU2 data collected before COVID-19  9.901049 0.2367851 Inf  9.436959 10.365139
## 
## timefactor = 3, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.481578 0.2373316 Inf  9.016417  9.946739
##  FU2 data collected before COVID-19  9.852008 0.2370916 Inf  9.387317 10.316699
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.680237 0.2111884 Inf  9.266315 10.094158
##  FU2 data collected before COVID-19  9.860018 0.2245080 Inf  9.419990 10.300045
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.591902 0.2113467 Inf  9.177670 10.006134
##  FU2 data collected before COVID-19  9.617296 0.2246682 Inf  9.176954 10.057637
## 
## timefactor = 3, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.713859 0.2114756 Inf  9.299374 10.128343
##  FU2 data collected before COVID-19  9.861810 0.2246404 Inf  9.421523 10.302097
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.615090 0.2345247 Inf  9.155430 10.074750
##  FU2 data collected before COVID-19  9.751365 0.2356662 Inf  9.289468 10.213262
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.432041 0.2347164 Inf  8.972006  9.892077
##  FU2 data collected before COVID-19  9.544407 0.2358416 Inf  9.082166 10.006648
## 
## timefactor = 3, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.207144 0.2356735 Inf  8.745233  9.669056
##  FU2 data collected before COVID-19  9.568911 0.2360187 Inf  9.106323 10.031499
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelAnimals_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2930357 0.1217698 Inf  -2.406  0.0161
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3770496 0.1223617 Inf  -3.081  0.0021
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2505482 0.1230737 Inf  -2.036  0.0418
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1629443 0.1741475 Inf  -0.936  0.3494
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3352180 0.1745390 Inf  -1.921  0.0548
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3704299 0.1761686 Inf  -2.103  0.0355
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1797814 0.1286643 Inf  -1.397  0.1623
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0253933 0.1292888 Inf  -0.196  0.8443
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1479514 0.1293990 Inf  -1.143  0.2529
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1362749 0.1694833 Inf  -0.804  0.4214
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1123653 0.1700299 Inf  -0.661  0.5087
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3617668 0.1715347 Inf  -2.109  0.0349
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelAnimals_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.2930357 0.1217698 Inf -0.5317001 -0.05437130
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.3770496 0.1223617 Inf -0.6168741 -0.13722505
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.2505482 0.1230737 Inf -0.4917681 -0.00932832
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.1629443 0.1741475 Inf -0.5042671  0.17837850
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.3352180 0.1745390 Inf -0.6773081  0.00687208
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.3704299 0.1761686 Inf -0.7157140 -0.02514580
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.1797814 0.1286643 Inf -0.4319588  0.07239596
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.0253933 0.1292888 Inf -0.2787947  0.22800805
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.1479514 0.1293990 Inf -0.4015688  0.10566592
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.1362749 0.1694833 Inf -0.4684562  0.19590626
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.1123653 0.1700299 Inf -0.4456178  0.22088731
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL   asymp.UCL
##  -0.3617668 0.1715347 Inf -0.6979686 -0.02556500
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.5.4.3) Graph of estimated marginal means

Animals_lsmeans_11 <- summary(lsmeans(modelAnimals_11, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.

Animals_lsmeans_11$Time<-NA
Animals_lsmeans_11$Time[Animals_lsmeans_11$timefactor==1]<-"Baseline"
Animals_lsmeans_11$Time[Animals_lsmeans_11$timefactor==2]<-"Follow-up 1"
Animals_lsmeans_11$Time[Animals_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(Animals_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "Animal Fluency (words)", title = "Animal Fluency Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

16.6) Age and sex differences in time x pandemic effects on cognitive functon ( adjusting for baseline)

All models use normalized cognitive scores. Each model is adjusted for education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, baseline PASE score, and baseline cognitive performance

16.6.1) RVLT Immediate Recall

16.6.1.1) Model

modelRVLT_imm_adj11<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +
                            (1|ID), data= truncated.data_long_2)
summary(modelRVLT_imm_adj11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Immediate_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +  
##     (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 70675.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6842 -0.5711 -0.0390  0.5248  3.8767 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 3.969    1.992   
##  Residual             7.501    2.739   
## Number of obs: 13535, groups:  ID, 6973
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                6.419e+00
## timefactor2                                                                4.083e-01
## PandemicFU2 data collected before COVID-19                                 6.392e-02
## Age_sexFemales 65+                                                        -1.020e-01
## Age_sexMales 45-64                                                        -6.870e-01
## Age_sexMales 65+                                                          -7.001e-01
## EducationHigh School Diploma                                               2.785e-01
## EducationLess than High School Diploma                                     4.132e-01
## EducationSome College                                                      1.261e-01
## EthnicityWhite                                                             6.143e-01
## IncomeLevel>$150k                                                          4.011e-01
## IncomeLevel$100-150k                                                       2.917e-01
## IncomeLevel$20-50k                                                         1.548e-01
## IncomeLevel$50-100k                                                        4.087e-01
## BMI                                                                       -1.458e-02
## CESD.10baseline                                                           -2.201e-02
## SmokingStatusFormer Smoker                                                 2.137e-01
## SmokingStatusNever Smoked                                                  3.353e-01
## SmokingStatusOccasional Smoker                                             3.579e-02
## RelationshipstatusMarried                                                  3.378e-01
## RelationshipstatusSeparated                                                3.182e-01
## RelationshipstatusSingle                                                   2.442e-01
## RelationshipstatusWidowed                                                 -1.827e-02
## LivingstatusAssisted Living                                               -9.862e-01
## LivingstatusHouse                                                         -3.390e-02
## LivingstatusOther                                                         -5.054e-01
## AnxietyYes                                                                 6.142e-02
## MoodDisordYes                                                             -1.045e-01
## Chronicconditions                                                         -3.015e-02
## PASE_TOTALbaseline                                                         2.510e-03
## RVLT_Immediate_Normedbaseline                                              3.543e-01
## timefactor2:PandemicFU2 data collected before COVID-19                    -1.805e-01
## timefactor2:Age_sexFemales 65+                                            -4.964e-01
## timefactor2:Age_sexMales 45-64                                             9.972e-02
## timefactor2:Age_sexMales 65+                                              -4.198e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.279e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.283e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                6.785e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  9.935e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.135e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    4.156e-01
##                                                                           Std. Error
## (Intercept)                                                                3.758e-01
## timefactor2                                                                1.204e-01
## PandemicFU2 data collected before COVID-19                                 1.428e-01
## Age_sexFemales 65+                                                         1.897e-01
## Age_sexMales 45-64                                                         1.386e-01
## Age_sexMales 65+                                                           1.761e-01
## EducationHigh School Diploma                                               1.064e-01
## EducationLess than High School Diploma                                     1.514e-01
## EducationSome College                                                      1.319e-01
## EthnicityWhite                                                             2.106e-01
## IncomeLevel>$150k                                                          1.948e-01
## IncomeLevel$100-150k                                                       1.578e-01
## IncomeLevel$20-50k                                                         1.067e-01
## IncomeLevel$50-100k                                                        1.147e-01
## BMI                                                                        7.161e-03
## CESD.10baseline                                                            8.549e-03
## SmokingStatusFormer Smoker                                                 1.449e-01
## SmokingStatusNever Smoked                                                  1.510e-01
## SmokingStatusOccasional Smoker                                             2.852e-01
## RelationshipstatusMarried                                                  1.247e-01
## RelationshipstatusSeparated                                                2.416e-01
## RelationshipstatusSingle                                                   1.689e-01
## RelationshipstatusWidowed                                                  1.729e-01
## LivingstatusAssisted Living                                                5.251e-01
## LivingstatusHouse                                                          1.138e-01
## LivingstatusOther                                                          4.213e-01
## AnxietyYes                                                                 1.486e-01
## MoodDisordYes                                                              1.075e-01
## Chronicconditions                                                          1.740e-02
## PASE_TOTALbaseline                                                         4.884e-04
## RVLT_Immediate_Normedbaseline                                              8.982e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.648e-01
## timefactor2:Age_sexFemales 65+                                             2.090e-01
## timefactor2:Age_sexMales 45-64                                             1.564e-01
## timefactor2:Age_sexMales 65+                                               1.998e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.489e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              2.073e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.446e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.878e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.387e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.832e-01
##                                                                                   df
## (Intercept)                                                                7.220e+03
## timefactor2                                                                6.860e+03
## PandemicFU2 data collected before COVID-19                                 1.214e+04
## Age_sexFemales 65+                                                         1.165e+04
## Age_sexMales 45-64                                                         1.191e+04
## Age_sexMales 65+                                                           1.190e+04
## EducationHigh School Diploma                                               6.914e+03
## EducationLess than High School Diploma                                     7.040e+03
## EducationSome College                                                      6.834e+03
## EthnicityWhite                                                             6.836e+03
## IncomeLevel>$150k                                                          6.913e+03
## IncomeLevel$100-150k                                                       6.869e+03
## IncomeLevel$20-50k                                                         6.920e+03
## IncomeLevel$50-100k                                                        6.917e+03
## BMI                                                                        6.884e+03
## CESD.10baseline                                                            6.904e+03
## SmokingStatusFormer Smoker                                                 6.933e+03
## SmokingStatusNever Smoked                                                  6.934e+03
## SmokingStatusOccasional Smoker                                             6.804e+03
## RelationshipstatusMarried                                                  6.923e+03
## RelationshipstatusSeparated                                                6.985e+03
## RelationshipstatusSingle                                                   6.923e+03
## RelationshipstatusWidowed                                                  6.927e+03
## LivingstatusAssisted Living                                                6.926e+03
## LivingstatusHouse                                                          6.919e+03
## LivingstatusOther                                                          6.791e+03
## AnxietyYes                                                                 6.889e+03
## MoodDisordYes                                                              6.906e+03
## Chronicconditions                                                          6.906e+03
## PASE_TOTALbaseline                                                         6.885e+03
## RVLT_Immediate_Normedbaseline                                              6.921e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     6.790e+03
## timefactor2:Age_sexFemales 65+                                             6.854e+03
## timefactor2:Age_sexMales 45-64                                             6.820e+03
## timefactor2:Age_sexMales 65+                                               6.875e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.214e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.215e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.216e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  6.814e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  6.749e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    6.808e+03
##                                                                           t value
## (Intercept)                                                                17.081
## timefactor2                                                                 3.390
## PandemicFU2 data collected before COVID-19                                  0.448
## Age_sexFemales 65+                                                         -0.538
## Age_sexMales 45-64                                                         -4.958
## Age_sexMales 65+                                                           -3.976
## EducationHigh School Diploma                                                2.617
## EducationLess than High School Diploma                                      2.730
## EducationSome College                                                       0.956
## EthnicityWhite                                                              2.917
## IncomeLevel>$150k                                                           2.059
## IncomeLevel$100-150k                                                        1.848
## IncomeLevel$20-50k                                                          1.452
## IncomeLevel$50-100k                                                         3.562
## BMI                                                                        -2.036
## CESD.10baseline                                                            -2.574
## SmokingStatusFormer Smoker                                                  1.474
## SmokingStatusNever Smoked                                                   2.221
## SmokingStatusOccasional Smoker                                              0.126
## RelationshipstatusMarried                                                   2.709
## RelationshipstatusSeparated                                                 1.317
## RelationshipstatusSingle                                                    1.446
## RelationshipstatusWidowed                                                  -0.106
## LivingstatusAssisted Living                                                -1.878
## LivingstatusHouse                                                          -0.298
## LivingstatusOther                                                          -1.200
## AnxietyYes                                                                  0.413
## MoodDisordYes                                                              -0.972
## Chronicconditions                                                          -1.733
## PASE_TOTALbaseline                                                          5.140
## RVLT_Immediate_Normedbaseline                                              39.448
## timefactor2:PandemicFU2 data collected before COVID-19                     -1.096
## timefactor2:Age_sexFemales 65+                                             -2.375
## timefactor2:Age_sexMales 45-64                                              0.638
## timefactor2:Age_sexMales 65+                                               -2.101
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.514
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.619
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 0.277
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.345
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   0.894
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.467
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               0.000703
## PandemicFU2 data collected before COVID-19                                0.654369
## Age_sexFemales 65+                                                        0.590623
## Age_sexMales 45-64                                                        7.21e-07
## Age_sexMales 65+                                                          7.06e-05
## EducationHigh School Diploma                                              0.008883
## EducationLess than High School Diploma                                    0.006347
## EducationSome College                                                     0.339107
## EthnicityWhite                                                            0.003551
## IncomeLevel>$150k                                                         0.039571
## IncomeLevel$100-150k                                                      0.064584
## IncomeLevel$20-50k                                                        0.146668
## IncomeLevel$50-100k                                                       0.000371
## BMI                                                                       0.041772
## CESD.10baseline                                                           0.010075
## SmokingStatusFormer Smoker                                                0.140424
## SmokingStatusNever Smoked                                                 0.026361
## SmokingStatusOccasional Smoker                                            0.900129
## RelationshipstatusMarried                                                 0.006756
## RelationshipstatusSeparated                                               0.187821
## RelationshipstatusSingle                                                  0.148248
## RelationshipstatusWidowed                                                 0.915818
## LivingstatusAssisted Living                                               0.060430
## LivingstatusHouse                                                         0.765724
## LivingstatusOther                                                         0.230252
## AnxietyYes                                                                0.679422
## MoodDisordYes                                                             0.330894
## Chronicconditions                                                         0.083121
## PASE_TOTALbaseline                                                        2.83e-07
## RVLT_Immediate_Normedbaseline                                              < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                    0.273332
## timefactor2:Age_sexFemales 65+                                            0.017580
## timefactor2:Age_sexMales 45-64                                            0.523815
## timefactor2:Age_sexMales 65+                                              0.035651
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.607355
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.535879
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.781483
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.729926
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.371132
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.142294
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                   
## Age_sexFemales 65+                                                           
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          ***
## EducationHigh School Diploma                                              ** 
## EducationLess than High School Diploma                                    ** 
## EducationSome College                                                        
## EthnicityWhite                                                            ** 
## IncomeLevel>$150k                                                         *  
## IncomeLevel$100-150k                                                      .  
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       *  
## CESD.10baseline                                                           *  
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                 *  
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                 ** 
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                               .  
## LivingstatusHouse                                                            
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         .  
## PASE_TOTALbaseline                                                        ***
## RVLT_Immediate_Normedbaseline                                             ***
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                            *  
## timefactor2:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                              *  
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.6.1.2) Estimated marginal means

Significantly lower RVLT immediate for males 65+

lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.647131 0.2484999 Inf 10.160081  11.13418
##  FU2 data collected before COVID-19 10.711050 0.2479757 Inf 10.225026  11.19707
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.055400 0.2492345 Inf 10.566909  11.54389
##  FU2 data collected before COVID-19 10.938824 0.2479555 Inf 10.452840  11.42481
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.545094 0.2702757 Inf 10.015363  11.07482
##  FU2 data collected before COVID-19 10.736920 0.2710850 Inf 10.205603  11.26824
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.456998 0.2718546 Inf  9.924173  10.98982
##  FU2 data collected before COVID-19 10.567679 0.2715214 Inf 10.035507  11.09985
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.960145 0.2406574 Inf  9.488465  10.43182
##  FU2 data collected before COVID-19 10.152377 0.2563105 Inf  9.650017  10.65474
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.468137 0.2408704 Inf  9.996039  10.94023
##  FU2 data collected before COVID-19 10.693349 0.2562374 Inf 10.191133  11.19557
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.947081 0.2681867 Inf  9.421445  10.47272
##  FU2 data collected before COVID-19 10.078848 0.2697431 Inf  9.550161  10.60753
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.935507 0.2699631 Inf  9.406389  10.46462
##  FU2 data collected before COVID-19 10.302399 0.2698663 Inf  9.773471  10.83133
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0639181 0.1427662 Inf  -0.448  0.6544
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   0.1165756 0.1442439 Inf   0.808  0.4190
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1918261 0.2043721 Inf  -0.939  0.3479
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1106807 0.2071280 Inf  -0.534  0.5931
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1922317 0.1508459 Inf  -1.274  0.2025
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2252127 0.1508996 Inf  -1.492  0.1356
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1317663 0.1988721 Inf  -0.663  0.5076
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3668923 0.2012933 Inf  -1.823  0.0684
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.0639181 0.1427662 Inf -0.3437347 0.2158984
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##   0.1165756 0.1442439 Inf -0.1661373 0.3992886
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.1918261 0.2043721 Inf -0.5923880 0.2087358
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.1106807 0.2071280 Inf -0.5166441 0.2952828
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.1922317 0.1508459 Inf -0.4878843 0.1034209
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.2252127 0.1508996 Inf -0.5209705 0.0705452
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.1317663 0.1988721 Inf -0.5215485 0.2580159
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.3668923 0.2012933 Inf -0.7614198 0.0276352
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.6.1.3) Graph of estimated marginal means

RVLTimmediate_lsmeans_adj11 <- summary(lsmeans(modelRVLT_imm_adj11, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTimmediate_lsmeans_adj11$Time<-NA
RVLTimmediate_lsmeans_adj11$Time[RVLTimmediate_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj11$Time[RVLTimmediate_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

16.6.2) RVLT Delayed Recall

16.6.2.1) Model

modelRVLT_del_adj11<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +
                            (1|ID), data= truncated.data_long_2)
summary(modelRVLT_del_adj11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +  
##     (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 69356.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9609 -0.5522 -0.0351  0.5078  3.9656 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 4.105    2.026   
##  Residual             6.899    2.627   
## Number of obs: 13415, groups:  ID, 6961
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                6.148e+00
## timefactor2                                                                8.000e-01
## PandemicFU2 data collected before COVID-19                                 1.582e-01
## Age_sexFemales 65+                                                         1.129e-01
## Age_sexMales 45-64                                                        -4.896e-01
## Age_sexMales 65+                                                          -4.101e-01
## EducationHigh School Diploma                                               2.166e-01
## EducationLess than High School Diploma                                     2.815e-01
## EducationSome College                                                      3.141e-01
## EthnicityWhite                                                             6.586e-01
## IncomeLevel>$150k                                                          2.668e-01
## IncomeLevel$100-150k                                                       1.725e-01
## IncomeLevel$20-50k                                                         1.689e-01
## IncomeLevel$50-100k                                                        4.474e-01
## BMI                                                                       -1.715e-02
## CESD.10baseline                                                           -1.956e-02
## SmokingStatusFormer Smoker                                                 1.046e-01
## SmokingStatusNever Smoked                                                  3.164e-01
## SmokingStatusOccasional Smoker                                             2.048e-01
## RelationshipstatusMarried                                                  5.045e-02
## RelationshipstatusSeparated                                               -9.984e-02
## RelationshipstatusSingle                                                  -3.306e-02
## RelationshipstatusWidowed                                                 -1.919e-01
## LivingstatusAssisted Living                                               -1.296e+00
## LivingstatusHouse                                                          5.338e-02
## LivingstatusOther                                                         -1.715e-01
## AnxietyYes                                                                 5.713e-02
## MoodDisordYes                                                             -1.087e-01
## Chronicconditions                                                         -3.436e-02
## PASE_TOTALbaseline                                                         2.659e-03
## RVLT_Delayed_Normedbaseline                                                3.942e-01
## timefactor2:PandemicFU2 data collected before COVID-19                    -4.264e-01
## timefactor2:Age_sexFemales 65+                                            -6.196e-01
## timefactor2:Age_sexMales 45-64                                            -8.355e-02
## timefactor2:Age_sexMales 65+                                              -5.885e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.142e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              9.945e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                8.237e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  3.722e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.475e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    3.678e-01
##                                                                           Std. Error
## (Intercept)                                                                3.753e-01
## timefactor2                                                                1.158e-01
## PandemicFU2 data collected before COVID-19                                 1.401e-01
## Age_sexFemales 65+                                                         1.865e-01
## Age_sexMales 45-64                                                         1.363e-01
## Age_sexMales 65+                                                           1.738e-01
## EducationHigh School Diploma                                               1.056e-01
## EducationLess than High School Diploma                                     1.507e-01
## EducationSome College                                                      1.306e-01
## EthnicityWhite                                                             2.103e-01
## IncomeLevel>$150k                                                          1.930e-01
## IncomeLevel$100-150k                                                       1.565e-01
## IncomeLevel$20-50k                                                         1.059e-01
## IncomeLevel$50-100k                                                        1.138e-01
## BMI                                                                        7.097e-03
## CESD.10baseline                                                            8.490e-03
## SmokingStatusFormer Smoker                                                 1.435e-01
## SmokingStatusNever Smoked                                                  1.495e-01
## SmokingStatusOccasional Smoker                                             2.832e-01
## RelationshipstatusMarried                                                  1.239e-01
## RelationshipstatusSeparated                                                2.399e-01
## RelationshipstatusSingle                                                   1.675e-01
## RelationshipstatusWidowed                                                  1.717e-01
## LivingstatusAssisted Living                                                5.220e-01
## LivingstatusHouse                                                          1.130e-01
## LivingstatusOther                                                          4.194e-01
## AnxietyYes                                                                 1.475e-01
## MoodDisordYes                                                              1.065e-01
## Chronicconditions                                                          1.725e-02
## PASE_TOTALbaseline                                                         4.848e-04
## RVLT_Delayed_Normedbaseline                                                9.104e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.584e-01
## timefactor2:Age_sexFemales 65+                                             2.021e-01
## timefactor2:Age_sexMales 45-64                                             1.506e-01
## timefactor2:Age_sexMales 65+                                               1.935e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.445e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              2.036e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.411e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  2.779e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.298e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    2.740e-01
##                                                                                   df
## (Intercept)                                                                7.230e+03
## timefactor2                                                                6.751e+03
## PandemicFU2 data collected before COVID-19                                 1.187e+04
## Age_sexFemales 65+                                                         1.137e+04
## Age_sexMales 45-64                                                         1.167e+04
## Age_sexMales 65+                                                           1.169e+04
## EducationHigh School Diploma                                               6.899e+03
## EducationLess than High School Diploma                                     7.063e+03
## EducationSome College                                                      6.797e+03
## EthnicityWhite                                                             6.920e+03
## IncomeLevel>$150k                                                          6.885e+03
## IncomeLevel$100-150k                                                       6.852e+03
## IncomeLevel$20-50k                                                         6.911e+03
## IncomeLevel$50-100k                                                        6.894e+03
## BMI                                                                        6.844e+03
## CESD.10baseline                                                            6.906e+03
## SmokingStatusFormer Smoker                                                 6.898e+03
## SmokingStatusNever Smoked                                                  6.899e+03
## SmokingStatusOccasional Smoker                                             6.825e+03
## RelationshipstatusMarried                                                  6.886e+03
## RelationshipstatusSeparated                                                6.994e+03
## RelationshipstatusSingle                                                   6.878e+03
## RelationshipstatusWidowed                                                  6.904e+03
## LivingstatusAssisted Living                                                6.965e+03
## LivingstatusHouse                                                          6.916e+03
## LivingstatusOther                                                          6.852e+03
## AnxietyYes                                                                 6.865e+03
## MoodDisordYes                                                              6.871e+03
## Chronicconditions                                                          6.872e+03
## PASE_TOTALbaseline                                                         6.882e+03
## RVLT_Delayed_Normedbaseline                                                6.888e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     6.685e+03
## timefactor2:Age_sexFemales 65+                                             6.798e+03
## timefactor2:Age_sexMales 45-64                                             6.731e+03
## timefactor2:Age_sexMales 65+                                               6.837e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.188e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.189e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.194e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  6.749e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  6.661e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    6.760e+03
##                                                                           t value
## (Intercept)                                                                16.381
## timefactor2                                                                 6.908
## PandemicFU2 data collected before COVID-19                                  1.129
## Age_sexFemales 65+                                                          0.605
## Age_sexMales 45-64                                                         -3.592
## Age_sexMales 65+                                                           -2.360
## EducationHigh School Diploma                                                2.051
## EducationLess than High School Diploma                                      1.868
## EducationSome College                                                       2.404
## EthnicityWhite                                                              3.131
## IncomeLevel>$150k                                                           1.382
## IncomeLevel$100-150k                                                        1.102
## IncomeLevel$20-50k                                                          1.595
## IncomeLevel$50-100k                                                         3.931
## BMI                                                                        -2.416
## CESD.10baseline                                                            -2.304
## SmokingStatusFormer Smoker                                                  0.729
## SmokingStatusNever Smoked                                                   2.117
## SmokingStatusOccasional Smoker                                              0.723
## RelationshipstatusMarried                                                   0.407
## RelationshipstatusSeparated                                                -0.416
## RelationshipstatusSingle                                                   -0.197
## RelationshipstatusWidowed                                                  -1.118
## LivingstatusAssisted Living                                                -2.483
## LivingstatusHouse                                                           0.472
## LivingstatusOther                                                          -0.409
## AnxietyYes                                                                  0.387
## MoodDisordYes                                                              -1.021
## Chronicconditions                                                          -1.992
## PASE_TOTALbaseline                                                          5.484
## RVLT_Delayed_Normedbaseline                                                43.300
## timefactor2:PandemicFU2 data collected before COVID-19                     -2.692
## timefactor2:Age_sexFemales 65+                                             -3.065
## timefactor2:Age_sexMales 45-64                                             -0.555
## timefactor2:Age_sexMales 65+                                               -3.041
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.876
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.489
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 0.342
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   1.339
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   1.077
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.342
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               5.37e-12
## PandemicFU2 data collected before COVID-19                                 0.25897
## Age_sexFemales 65+                                                         0.54491
## Age_sexMales 45-64                                                         0.00033
## Age_sexMales 65+                                                           0.01828
## EducationHigh School Diploma                                               0.04030
## EducationLess than High School Diploma                                     0.06180
## EducationSome College                                                      0.01624
## EthnicityWhite                                                             0.00175
## IncomeLevel>$150k                                                          0.16691
## IncomeLevel$100-150k                                                       0.27060
## IncomeLevel$20-50k                                                         0.11068
## IncomeLevel$50-100k                                                       8.55e-05
## BMI                                                                        0.01572
## CESD.10baseline                                                            0.02124
## SmokingStatusFormer Smoker                                                 0.46600
## SmokingStatusNever Smoked                                                  0.03432
## SmokingStatusOccasional Smoker                                             0.46959
## RelationshipstatusMarried                                                  0.68378
## RelationshipstatusSeparated                                                0.67724
## RelationshipstatusSingle                                                   0.84355
## RelationshipstatusWidowed                                                  0.26371
## LivingstatusAssisted Living                                                0.01304
## LivingstatusHouse                                                          0.63660
## LivingstatusOther                                                          0.68268
## AnxietyYes                                                                 0.69850
## MoodDisordYes                                                              0.30728
## Chronicconditions                                                          0.04644
## PASE_TOTALbaseline                                                        4.31e-08
## RVLT_Delayed_Normedbaseline                                                < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                     0.00712
## timefactor2:Age_sexFemales 65+                                             0.00218
## timefactor2:Age_sexMales 45-64                                             0.57920
## timefactor2:Age_sexMales 65+                                               0.00237
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              0.38102
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              0.62520
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                0.73264
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  0.18053
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  0.28139
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    0.17961
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                   
## Age_sexFemales 65+                                                           
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          *  
## EducationHigh School Diploma                                              *  
## EducationLess than High School Diploma                                    .  
## EducationSome College                                                     *  
## EthnicityWhite                                                            ** 
## IncomeLevel>$150k                                                            
## IncomeLevel$100-150k                                                         
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       *  
## CESD.10baseline                                                           *  
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                 *  
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                               *  
## LivingstatusHouse                                                            
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         *  
## PASE_TOTALbaseline                                                        ***
## RVLT_Delayed_Normedbaseline                                               ***
## timefactor2:PandemicFU2 data collected before COVID-19                    ** 
## timefactor2:Age_sexFemales 65+                                            ** 
## timefactor2:Age_sexMales 45-64                                               
## timefactor2:Age_sexMales 65+                                              ** 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.6.2.2) Estimated marginal means

lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.63930 0.2467899 Inf 10.155596  11.12299
##  FU2 data collected before COVID-19 10.79750 0.2460794 Inf 10.315189  11.27980
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  11.43926 0.2472123 Inf 10.954731  11.92379
##  FU2 data collected before COVID-19 11.17108 0.2461349 Inf 10.688665  11.65350
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.75218 0.2677506 Inf 10.227402  11.27697
##  FU2 data collected before COVID-19 11.12462 0.2688596 Inf 10.597662  11.65157
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.93256 0.2703290 Inf 10.402729  11.46240
##  FU2 data collected before COVID-19 11.25081 0.2692886 Inf 10.723019  11.77861
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.14967 0.2390858 Inf  9.681072  10.61827
##  FU2 data collected before COVID-19 10.40732 0.2541979 Inf  9.909106  10.90554
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.86609 0.2391310 Inf 10.397399  11.33478
##  FU2 data collected before COVID-19 10.94491 0.2542058 Inf 10.446673  11.44314
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.22915 0.2662749 Inf  9.707259  10.75104
##  FU2 data collected before COVID-19 10.46972 0.2679588 Inf  9.944529  10.99491
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.44062 0.2679346 Inf  9.915474  10.96576
##  FU2 data collected before COVID-19 10.62261 0.2676523 Inf 10.098019  11.14720
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1582006 0.1401405 Inf  -1.129  0.2590
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   0.2681783 0.1413314 Inf   1.898  0.0578
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3724328 0.2008420 Inf  -1.854  0.0637
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3182511 0.2043285 Inf  -1.558  0.1193
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2576531 0.1482219 Inf  -1.738  0.0822
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0788205 0.1482675 Inf  -0.532  0.5950
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.2405706 0.1964722 Inf  -1.224  0.2208
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1819915 0.1984004 Inf  -0.917  0.3590
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.1582006 0.1401405 Inf -0.4328709 0.1164697
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##   0.2681783 0.1413314 Inf -0.0088261 0.5451828
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.3724328 0.2008420 Inf -0.7660758 0.0212102
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.3182511 0.2043285 Inf -0.7187277 0.0822254
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.2576531 0.1482219 Inf -0.5481626 0.0328564
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.0788205 0.1482675 Inf -0.3694196 0.2117785
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.2405706 0.1964722 Inf -0.6256491 0.1445079
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.1819915 0.1984004 Inf -0.5708491 0.2068660
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.6.2.3) Graph of estimated marginal means

RVLTdelayed_lsmeans_adj11 <- summary(lsmeans(modelRVLT_del_adj11, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.

RVLTdelayed_lsmeans_adj11$Time<-NA
RVLTdelayed_lsmeans_adj11$Time[RVLTdelayed_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj11$Time[RVLTdelayed_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

16.6.3) Mental Alteration Test

16.6.3.1) Model

modelMAT_adj11<- lmer(MAT_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + MAT_Normedbaseline +
                      (1|ID), data= truncated.data_long_2)
summary(modelMAT_adj11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## MAT_Normed ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +  
##     IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +  
##     Livingstatus + Anxiety + MoodDisord + Chronicconditions +  
##     PASE_TOTALbaseline + MAT_Normedbaseline + (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 65712.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2923 -0.5209 -0.0531  0.3969  4.4907 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.583    1.607   
##  Residual             8.337    2.887   
## Number of obs: 12616, groups:  ID, 6874
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                8.347e+00
## timefactor2                                                               -3.248e+00
## PandemicFU2 data collected before COVID-19                                -1.257e+00
## Age_sexFemales 65+                                                        -6.694e-01
## Age_sexMales 45-64                                                        -3.233e+00
## Age_sexMales 65+                                                          -3.301e+00
## EducationHigh School Diploma                                              -5.268e-02
## EducationLess than High School Diploma                                    -1.556e-01
## EducationSome College                                                     -4.562e-02
## EthnicityWhite                                                             8.511e-01
## IncomeLevel>$150k                                                          1.278e-01
## IncomeLevel$100-150k                                                       1.582e-01
## IncomeLevel$20-50k                                                         1.493e-01
## IncomeLevel$50-100k                                                        1.341e-01
## BMI                                                                       -1.124e-02
## CESD.10baseline                                                           -2.172e-02
## SmokingStatusFormer Smoker                                                 1.522e-02
## SmokingStatusNever Smoked                                                 -6.242e-02
## SmokingStatusOccasional Smoker                                             1.890e-01
## RelationshipstatusMarried                                                 -2.531e-02
## RelationshipstatusSeparated                                               -8.381e-02
## RelationshipstatusSingle                                                   5.368e-01
## RelationshipstatusWidowed                                                 -2.280e-01
## LivingstatusAssisted Living                                               -1.794e-01
## LivingstatusHouse                                                         -1.703e-01
## LivingstatusOther                                                         -2.286e-01
## AnxietyYes                                                                 7.590e-02
## MoodDisordYes                                                              1.099e-01
## Chronicconditions                                                         -3.439e-02
## PASE_TOTALbaseline                                                        -3.153e-04
## MAT_Normedbaseline                                                         4.417e-01
## timefactor2:PandemicFU2 data collected before COVID-19                     1.418e+00
## timefactor2:Age_sexFemales 65+                                             4.689e-01
## timefactor2:Age_sexMales 45-64                                             3.291e+00
## timefactor2:Age_sexMales 65+                                               2.884e+00
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              3.934e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.309e+00
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.557e+00
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.594e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.480e+00
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   -1.321e+00
##                                                                           Std. Error
## (Intercept)                                                                3.681e-01
## timefactor2                                                                1.299e-01
## PandemicFU2 data collected before COVID-19                                 1.429e-01
## Age_sexFemales 65+                                                         1.934e-01
## Age_sexMales 45-64                                                         1.388e-01
## Age_sexMales 65+                                                           1.799e-01
## EducationHigh School Diploma                                               1.027e-01
## EducationLess than High School Diploma                                     1.486e-01
## EducationSome College                                                      1.264e-01
## EthnicityWhite                                                             2.052e-01
## IncomeLevel>$150k                                                          1.873e-01
## IncomeLevel$100-150k                                                       1.519e-01
## IncomeLevel$20-50k                                                         1.033e-01
## IncomeLevel$50-100k                                                        1.110e-01
## BMI                                                                        6.878e-03
## CESD.10baseline                                                            8.249e-03
## SmokingStatusFormer Smoker                                                 1.389e-01
## SmokingStatusNever Smoked                                                  1.447e-01
## SmokingStatusOccasional Smoker                                             2.741e-01
## RelationshipstatusMarried                                                  1.205e-01
## RelationshipstatusSeparated                                                2.317e-01
## RelationshipstatusSingle                                                   1.629e-01
## RelationshipstatusWidowed                                                  1.680e-01
## LivingstatusAssisted Living                                                5.081e-01
## LivingstatusHouse                                                          1.102e-01
## LivingstatusOther                                                          4.105e-01
## AnxietyYes                                                                 1.428e-01
## MoodDisordYes                                                              1.031e-01
## Chronicconditions                                                          1.682e-02
## PASE_TOTALbaseline                                                         4.690e-04
## MAT_Normedbaseline                                                         9.548e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.779e-01
## timefactor2:Age_sexFemales 65+                                             2.328e-01
## timefactor2:Age_sexMales 45-64                                             1.694e-01
## timefactor2:Age_sexMales 65+                                               2.231e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              2.541e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              2.089e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                2.503e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  3.187e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.596e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    3.147e-01
##                                                                                   df
## (Intercept)                                                                7.098e+03
## timefactor2                                                                6.317e+03
## PandemicFU2 data collected before COVID-19                                 1.208e+04
## Age_sexFemales 65+                                                         1.183e+04
## Age_sexMales 45-64                                                         1.192e+04
## Age_sexMales 65+                                                           1.195e+04
## EducationHigh School Diploma                                               6.637e+03
## EducationLess than High School Diploma                                     6.853e+03
## EducationSome College                                                      6.555e+03
## EthnicityWhite                                                             6.723e+03
## IncomeLevel>$150k                                                          6.613e+03
## IncomeLevel$100-150k                                                       6.602e+03
## IncomeLevel$20-50k                                                         6.689e+03
## IncomeLevel$50-100k                                                        6.649e+03
## BMI                                                                        6.570e+03
## CESD.10baseline                                                            6.615e+03
## SmokingStatusFormer Smoker                                                 6.575e+03
## SmokingStatusNever Smoked                                                  6.578e+03
## SmokingStatusOccasional Smoker                                             6.441e+03
## RelationshipstatusMarried                                                  6.699e+03
## RelationshipstatusSeparated                                                6.693e+03
## RelationshipstatusSingle                                                   6.672e+03
## RelationshipstatusWidowed                                                  6.758e+03
## LivingstatusAssisted Living                                                6.556e+03
## LivingstatusHouse                                                          6.708e+03
## LivingstatusOther                                                          6.680e+03
## AnxietyYes                                                                 6.615e+03
## MoodDisordYes                                                              6.583e+03
## Chronicconditions                                                          6.654e+03
## PASE_TOTALbaseline                                                         6.589e+03
## MAT_Normedbaseline                                                         6.684e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     6.249e+03
## timefactor2:Age_sexFemales 65+                                             6.637e+03
## timefactor2:Age_sexMales 45-64                                             6.312e+03
## timefactor2:Age_sexMales 65+                                               6.597e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.214e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.211e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.214e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  6.541e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  6.290e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    6.501e+03
##                                                                           t value
## (Intercept)                                                                22.678
## timefactor2                                                               -25.001
## PandemicFU2 data collected before COVID-19                                 -8.795
## Age_sexFemales 65+                                                         -3.462
## Age_sexMales 45-64                                                        -23.300
## Age_sexMales 65+                                                          -18.352
## EducationHigh School Diploma                                               -0.513
## EducationLess than High School Diploma                                     -1.047
## EducationSome College                                                      -0.361
## EthnicityWhite                                                              4.147
## IncomeLevel>$150k                                                           0.682
## IncomeLevel$100-150k                                                        1.041
## IncomeLevel$20-50k                                                          1.446
## IncomeLevel$50-100k                                                         1.208
## BMI                                                                        -1.634
## CESD.10baseline                                                            -2.634
## SmokingStatusFormer Smoker                                                  0.110
## SmokingStatusNever Smoked                                                  -0.431
## SmokingStatusOccasional Smoker                                              0.690
## RelationshipstatusMarried                                                  -0.210
## RelationshipstatusSeparated                                                -0.362
## RelationshipstatusSingle                                                    3.296
## RelationshipstatusWidowed                                                  -1.358
## LivingstatusAssisted Living                                                -0.353
## LivingstatusHouse                                                          -1.546
## LivingstatusOther                                                          -0.557
## AnxietyYes                                                                  0.532
## MoodDisordYes                                                               1.066
## Chronicconditions                                                          -2.045
## PASE_TOTALbaseline                                                         -0.672
## MAT_Normedbaseline                                                         46.264
## timefactor2:PandemicFU2 data collected before COVID-19                      7.974
## timefactor2:Age_sexFemales 65+                                              2.014
## timefactor2:Age_sexMales 45-64                                             19.426
## timefactor2:Age_sexMales 65+                                               12.930
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.155
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               6.268
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 6.221
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.500
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -5.699
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    -4.198
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                                < 2e-16
## PandemicFU2 data collected before COVID-19                                 < 2e-16
## Age_sexFemales 65+                                                        0.000539
## Age_sexMales 45-64                                                         < 2e-16
## Age_sexMales 65+                                                           < 2e-16
## EducationHigh School Diploma                                              0.608106
## EducationLess than High School Diploma                                    0.295088
## EducationSome College                                                     0.718135
## EthnicityWhite                                                            3.41e-05
## IncomeLevel>$150k                                                         0.495147
## IncomeLevel$100-150k                                                      0.297709
## IncomeLevel$20-50k                                                        0.148176
## IncomeLevel$50-100k                                                       0.227092
## BMI                                                                       0.102390
## CESD.10baseline                                                           0.008468
## SmokingStatusFormer Smoker                                                0.912753
## SmokingStatusNever Smoked                                                 0.666243
## SmokingStatusOccasional Smoker                                            0.490462
## RelationshipstatusMarried                                                 0.833712
## RelationshipstatusSeparated                                               0.717577
## RelationshipstatusSingle                                                  0.000986
## RelationshipstatusWidowed                                                 0.174660
## LivingstatusAssisted Living                                               0.724074
## LivingstatusHouse                                                         0.122258
## LivingstatusOther                                                         0.577619
## AnxietyYes                                                                0.595041
## MoodDisordYes                                                             0.286426
## Chronicconditions                                                         0.040894
## PASE_TOTALbaseline                                                        0.501426
## MAT_Normedbaseline                                                         < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                    1.82e-15
## timefactor2:Age_sexFemales 65+                                            0.044036
## timefactor2:Age_sexMales 45-64                                             < 2e-16
## timefactor2:Age_sexMales 65+                                               < 2e-16
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.876985
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             3.78e-10
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               5.10e-10
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.616990
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.26e-08
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   2.73e-05
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                ***
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          ***
## EducationHigh School Diploma                                                 
## EducationLess than High School Diploma                                       
## EducationSome College                                                        
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                            
## IncomeLevel$100-150k                                                         
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                          
## BMI                                                                          
## CESD.10baseline                                                           ** 
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                  ***
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                            
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                         *  
## PASE_TOTALbaseline                                                           
## MAT_Normedbaseline                                                        ***
## timefactor2:PandemicFU2 data collected before COVID-19                    ***
## timefactor2:Age_sexFemales 65+                                            *  
## timefactor2:Age_sexMales 45-64                                            ***
## timefactor2:Age_sexMales 65+                                              ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             ***
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               ***
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 ***
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.6.3.2) Estimated marginal means

lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  12.744170 0.2424141 Inf 12.269047 13.219293
##  FU2 data collected before COVID-19 11.487398 0.2416037 Inf 11.013864 11.960933
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.496412 0.2425075 Inf  9.021106  9.971718
##  FU2 data collected before COVID-19  9.658130 0.2411418 Inf  9.185501 10.130759
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  12.074787 0.2675273 Inf 11.550444 12.599131
##  FU2 data collected before COVID-19 10.857355 0.2664621 Inf 10.335098 11.379611
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.295957 0.2677245 Inf  8.771226  9.820687
##  FU2 data collected before COVID-19  9.656410 0.2664143 Inf  9.134247 10.178572
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.511111 0.2344499 Inf  9.051598  9.970625
##  FU2 data collected before COVID-19  9.563801 0.2512334 Inf  9.071392 10.056209
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.554576 0.2340630 Inf  9.095821 10.013331
##  FU2 data collected before COVID-19  9.546113 0.2501548 Inf  9.055819 10.036408
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.443127 0.2654002 Inf  8.922952  9.963302
##  FU2 data collected before COVID-19  9.743585 0.2667682 Inf  9.220729 10.266441
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.079691 0.2675412 Inf  8.555320  9.604062
##  FU2 data collected before COVID-19  9.477504 0.2657853 Inf  8.956574  9.998433
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   1.2567714 0.1428988 Inf   8.795  <.0001
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.1617181 0.1430028 Inf  -1.131  0.2581
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   1.2174328 0.2105296 Inf   5.783  <.0001
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3604533 0.2116721 Inf  -1.703  0.0886
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.0526895 0.1527831 Inf  -0.345  0.7302
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##   0.0084627 0.1505752 Inf   0.056  0.9552
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3004582 0.2057308 Inf  -1.460  0.1442
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df z.ratio p.value
##  -0.3978132 0.2075758 Inf  -1.916  0.0553
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##   1.2567714 0.1428988 Inf  0.9766950 1.5368479
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.1617181 0.1430028 Inf -0.4419984 0.1185622
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##   1.2174328 0.2105296 Inf  0.8048023 1.6300633
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.3604533 0.2116721 Inf -0.7753229 0.0544164
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.0526895 0.1527831 Inf -0.3521388 0.2467598
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##   0.0084627 0.1505752 Inf -0.2866593 0.3035847
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.3004582 0.2057308 Inf -0.7036832 0.1027668
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate        SE  df  asymp.LCL asymp.UCL
##  -0.3978132 0.2075758 Inf -0.8046542 0.0090279
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.6.3.3) Graph of estimated marginal means

MAT_lsmeans_adj11 <- summary(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.

MAT_lsmeans_adj11$Time<-NA
MAT_lsmeans_adj11$Time[MAT_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj11$Time[MAT_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

16.6.4) Animal Fluency

16.6.4.1) Model

modelAnimals_adj11<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus + 
                            Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +
                           (1|ID), data= truncated.data_long_2)
summary(modelAnimals_adj11)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Animal_Fluency_Normed ~ timefactor * Pandemic * Age_sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +  
##     (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 60844.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8739 -0.5573 -0.0156  0.5352  4.1659 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2.364    1.538   
##  Residual             3.182    1.784   
## Number of obs: 13639, groups:  ID, 6978
## 
## Fixed effects:
##                                                                             Estimate
## (Intercept)                                                                4.161e+00
## timefactor2                                                                3.121e-01
## PandemicFU2 data collected before COVID-19                                 2.369e-01
## Age_sexFemales 65+                                                        -1.749e-01
## Age_sexMales 45-64                                                        -3.342e-02
## Age_sexMales 65+                                                          -1.282e-01
## EducationHigh School Diploma                                               1.759e-01
## EducationLess than High School Diploma                                     1.104e-01
## EducationSome College                                                      1.616e-01
## EthnicityWhite                                                             5.154e-01
## IncomeLevel>$150k                                                         -7.940e-02
## IncomeLevel$100-150k                                                      -1.919e-02
## IncomeLevel$20-50k                                                        -1.331e-01
## IncomeLevel$50-100k                                                       -6.360e-03
## BMI                                                                       -8.268e-03
## CESD.10baseline                                                           -1.057e-02
## SmokingStatusFormer Smoker                                                 1.979e-01
## SmokingStatusNever Smoked                                                  1.870e-01
## SmokingStatusOccasional Smoker                                             5.156e-02
## RelationshipstatusMarried                                                  2.691e-02
## RelationshipstatusSeparated                                                2.051e-01
## RelationshipstatusSingle                                                   8.506e-02
## RelationshipstatusWidowed                                                 -8.159e-02
## LivingstatusAssisted Living                                               -1.426e-01
## LivingstatusHouse                                                          1.448e-01
## LivingstatusOther                                                         -2.653e-01
## AnxietyYes                                                                 1.077e-01
## MoodDisordYes                                                              5.307e-02
## Chronicconditions                                                         -9.824e-03
## PASE_TOTALbaseline                                                         8.361e-04
## Animal_Fluency_Normedbaseline                                              5.201e-01
## timefactor2:PandemicFU2 data collected before COVID-19                    -1.278e-01
## timefactor2:Age_sexFemales 65+                                            -3.929e-01
## timefactor2:Age_sexMales 45-64                                            -1.890e-01
## timefactor2:Age_sexMales 65+                                              -5.324e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.124e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             -2.979e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               -1.799e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.616e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  2.478e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    3.717e-01
##                                                                           Std. Error
## (Intercept)                                                                2.708e-01
## timefactor2                                                                7.826e-02
## PandemicFU2 data collected before COVID-19                                 9.902e-02
## Age_sexFemales 65+                                                         1.316e-01
## Age_sexMales 45-64                                                         9.623e-02
## Age_sexMales 65+                                                           1.222e-01
## EducationHigh School Diploma                                               7.597e-02
## EducationLess than High School Diploma                                     1.077e-01
## EducationSome College                                                      9.408e-02
## EthnicityWhite                                                             1.506e-01
## IncomeLevel>$150k                                                          1.388e-01
## IncomeLevel$100-150k                                                       1.127e-01
## IncomeLevel$20-50k                                                         7.606e-02
## IncomeLevel$50-100k                                                        8.181e-02
## BMI                                                                        5.107e-03
## CESD.10baseline                                                            6.107e-03
## SmokingStatusFormer Smoker                                                 1.033e-01
## SmokingStatusNever Smoked                                                  1.076e-01
## SmokingStatusOccasional Smoker                                             2.039e-01
## RelationshipstatusMarried                                                  8.909e-02
## RelationshipstatusSeparated                                                1.721e-01
## RelationshipstatusSingle                                                   1.205e-01
## RelationshipstatusWidowed                                                  1.232e-01
## LivingstatusAssisted Living                                                3.753e-01
## LivingstatusHouse                                                          8.122e-02
## LivingstatusOther                                                          3.005e-01
## AnxietyYes                                                                 1.061e-01
## MoodDisordYes                                                              7.673e-02
## Chronicconditions                                                          1.240e-02
## PASE_TOTALbaseline                                                         3.483e-04
## Animal_Fluency_Normedbaseline                                              7.852e-03
## timefactor2:PandemicFU2 data collected before COVID-19                     1.070e-01
## timefactor2:Age_sexFemales 65+                                             1.356e-01
## timefactor2:Age_sexMales 45-64                                             1.018e-01
## timefactor2:Age_sexMales 65+                                               1.296e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.721e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.438e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.693e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  1.860e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  1.551e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    1.834e-01
##                                                                                   df
## (Intercept)                                                                7.173e+03
## timefactor2                                                                6.869e+03
## PandemicFU2 data collected before COVID-19                                 1.158e+04
## Age_sexFemales 65+                                                         1.107e+04
## Age_sexMales 45-64                                                         1.137e+04
## Age_sexMales 65+                                                           1.133e+04
## EducationHigh School Diploma                                               6.914e+03
## EducationLess than High School Diploma                                     6.990e+03
## EducationSome College                                                      6.858e+03
## EthnicityWhite                                                             6.852e+03
## IncomeLevel>$150k                                                          6.912e+03
## IncomeLevel$100-150k                                                       6.881e+03
## IncomeLevel$20-50k                                                         6.905e+03
## IncomeLevel$50-100k                                                        6.905e+03
## BMI                                                                        6.886e+03
## CESD.10baseline                                                            6.919e+03
## SmokingStatusFormer Smoker                                                 6.915e+03
## SmokingStatusNever Smoked                                                  6.915e+03
## SmokingStatusOccasional Smoker                                             6.840e+03
## RelationshipstatusMarried                                                  6.940e+03
## RelationshipstatusSeparated                                                6.962e+03
## RelationshipstatusSingle                                                   6.938e+03
## RelationshipstatusWidowed                                                  6.912e+03
## LivingstatusAssisted Living                                                6.965e+03
## LivingstatusHouse                                                          6.920e+03
## LivingstatusOther                                                          6.782e+03
## AnxietyYes                                                                 6.896e+03
## MoodDisordYes                                                              6.912e+03
## Chronicconditions                                                          6.886e+03
## PASE_TOTALbaseline                                                         6.895e+03
## Animal_Fluency_Normedbaseline                                              6.890e+03
## timefactor2:PandemicFU2 data collected before COVID-19                     6.801e+03
## timefactor2:Age_sexFemales 65+                                             6.850e+03
## timefactor2:Age_sexMales 45-64                                             6.847e+03
## timefactor2:Age_sexMales 65+                                               6.871e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              1.156e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              1.160e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                1.159e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  6.796e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  6.776e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    6.801e+03
##                                                                           t value
## (Intercept)                                                                15.363
## timefactor2                                                                 3.988
## PandemicFU2 data collected before COVID-19                                  2.392
## Age_sexFemales 65+                                                         -1.329
## Age_sexMales 45-64                                                         -0.347
## Age_sexMales 65+                                                           -1.049
## EducationHigh School Diploma                                                2.316
## EducationLess than High School Diploma                                      1.026
## EducationSome College                                                       1.717
## EthnicityWhite                                                              3.422
## IncomeLevel>$150k                                                          -0.572
## IncomeLevel$100-150k                                                       -0.170
## IncomeLevel$20-50k                                                         -1.750
## IncomeLevel$50-100k                                                        -0.078
## BMI                                                                        -1.619
## CESD.10baseline                                                            -1.730
## SmokingStatusFormer Smoker                                                  1.916
## SmokingStatusNever Smoked                                                   1.738
## SmokingStatusOccasional Smoker                                              0.253
## RelationshipstatusMarried                                                   0.302
## RelationshipstatusSeparated                                                 1.192
## RelationshipstatusSingle                                                    0.706
## RelationshipstatusWidowed                                                  -0.662
## LivingstatusAssisted Living                                                -0.380
## LivingstatusHouse                                                           1.783
## LivingstatusOther                                                          -0.883
## AnxietyYes                                                                  1.015
## MoodDisordYes                                                               0.692
## Chronicconditions                                                          -0.792
## PASE_TOTALbaseline                                                          2.401
## Animal_Fluency_Normedbaseline                                              66.245
## timefactor2:PandemicFU2 data collected before COVID-19                     -1.195
## timefactor2:Age_sexFemales 65+                                             -2.898
## timefactor2:Age_sexMales 45-64                                             -1.857
## timefactor2:Age_sexMales 65+                                               -4.109
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               0.065
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              -2.072
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -1.063
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.869
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   1.598
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     2.026
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               6.74e-05
## PandemicFU2 data collected before COVID-19                                0.016765
## Age_sexFemales 65+                                                        0.183882
## Age_sexMales 45-64                                                        0.728414
## Age_sexMales 65+                                                          0.293989
## EducationHigh School Diploma                                              0.020603
## EducationLess than High School Diploma                                    0.305091
## EducationSome College                                                     0.085969
## EthnicityWhite                                                            0.000625
## IncomeLevel>$150k                                                         0.567320
## IncomeLevel$100-150k                                                      0.864833
## IncomeLevel$20-50k                                                        0.080161
## IncomeLevel$50-100k                                                       0.938038
## BMI                                                                       0.105527
## CESD.10baseline                                                           0.083648
## SmokingStatusFormer Smoker                                                0.055380
## SmokingStatusNever Smoked                                                 0.082221
## SmokingStatusOccasional Smoker                                            0.800339
## RelationshipstatusMarried                                                 0.762643
## RelationshipstatusSeparated                                               0.233417
## RelationshipstatusSingle                                                  0.480329
## RelationshipstatusWidowed                                                 0.507731
## LivingstatusAssisted Living                                               0.703973
## LivingstatusHouse                                                         0.074687
## LivingstatusOther                                                         0.377377
## AnxietyYes                                                                0.310043
## MoodDisordYes                                                             0.489208
## Chronicconditions                                                         0.428297
## PASE_TOTALbaseline                                                        0.016396
## Animal_Fluency_Normedbaseline                                              < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                    0.232318
## timefactor2:Age_sexFemales 65+                                            0.003762
## timefactor2:Age_sexMales 45-64                                            0.063331
## timefactor2:Age_sexMales 65+                                              4.02e-05
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.947941
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.038316
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.287976
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.384943
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.110189
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.042776
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                *  
## Age_sexFemales 65+                                                           
## Age_sexMales 45-64                                                           
## Age_sexMales 65+                                                             
## EducationHigh School Diploma                                              *  
## EducationLess than High School Diploma                                       
## EducationSome College                                                     .  
## EthnicityWhite                                                            ***
## IncomeLevel>$150k                                                            
## IncomeLevel$100-150k                                                         
## IncomeLevel$20-50k                                                        .  
## IncomeLevel$50-100k                                                          
## BMI                                                                          
## CESD.10baseline                                                           .  
## SmokingStatusFormer Smoker                                                .  
## SmokingStatusNever Smoked                                                 .  
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         .  
## LivingstatusOther                                                            
## AnxietyYes                                                                   
## MoodDisordYes                                                                
## Chronicconditions                                                            
## PASE_TOTALbaseline                                                        *  
## Animal_Fluency_Normedbaseline                                             ***
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                            ** 
## timefactor2:Age_sexMales 45-64                                            .  
## timefactor2:Age_sexMales 65+                                              ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             *  
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.6.4.2) Estimated marginal means

lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.887211 0.1766120 Inf  9.541058 10.233365
##  FU2 data collected before COVID-19 10.124076 0.1762762 Inf  9.778581 10.469571
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  10.199304 0.1769695 Inf  9.852451 10.546158
##  FU2 data collected before COVID-19 10.308415 0.1763161 Inf  9.962842 10.653988
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.712325 0.1913307 Inf  9.337323 10.087326
##  FU2 data collected before COVID-19  9.960424 0.1915964 Inf  9.584902 10.335946
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.631510 0.1922524 Inf  9.254702 10.008318
##  FU2 data collected before COVID-19  9.913496 0.1919979 Inf  9.537187 10.289805
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.853796 0.1711636 Inf  9.518322 10.189271
##  FU2 data collected before COVID-19  9.792729 0.1818446 Inf  9.436320 10.149138
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.976909 0.1713103 Inf  9.641147 10.312671
##  FU2 data collected before COVID-19 10.035860 0.1818283 Inf  9.679483 10.392237
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.758995 0.1900380 Inf  9.386527 10.131463
##  FU2 data collected before COVID-19  9.815993 0.1908964 Inf  9.441843 10.190143
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                              lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19   9.538675 0.1911423 Inf  9.164043  9.913307
##  FU2 data collected before COVID-19  9.839597 0.1910988 Inf  9.465050 10.214143
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.23686421 0.09901692 Inf  -2.392  0.0167
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.10911054 0.09982673 Inf  -1.093  0.2744
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.24809925 0.14106932 Inf  -1.759  0.0786
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.28198632 0.14292097 Inf  -1.973  0.0485
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##   0.06106789 0.10461039 Inf   0.584  0.5594
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.05895032 0.10473753 Inf  -0.563  0.5735
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.05699842 0.13747589 Inf  -0.415  0.6784
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df z.ratio p.value
##  -0.30092188 0.13918288 Inf  -2.162  0.0306
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.23686421 0.09901692 Inf -0.4309338 -0.04279462
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.10911054 0.09982673 Inf -0.3047673  0.08654626
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.24809925 0.14106932 Inf -0.5245900  0.02839155
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.28198632 0.14292097 Inf -0.5621063 -0.00186636
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##   0.06106789 0.10461039 Inf -0.1439647  0.26610049
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.05895032 0.10473753 Inf -0.2642321  0.14633148
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.05699842 0.13747589 Inf -0.3264462  0.21244937
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##     estimate         SE  df  asymp.LCL   asymp.UCL
##  -0.30092188 0.13918288 Inf -0.5737153 -0.02812844
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.6.4.3) Graph of estimated marginal means

Animals_lsmeans_adj11 <- summary(lsmeans(modelAnimals_adj11, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.

Animals_lsmeans_adj11$Time<-NA
Animals_lsmeans_adj11$Time[Animals_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj11$Time[Animals_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

16.7) Physical Activity Results

16.7.1) Full model unadjusted for baseline

16.7.1.1) Model

modelPASE_5<- lmer(PASE_TOTAL~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
                         (1|ID), data= truncated.data_long)
summary(modelPASE_5)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PASE_TOTAL ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + (1 | ID)
##    Data: truncated.data_long
## 
## REML criterion at convergence: 117645.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4663 -0.5357 -0.0469  0.4911  6.5485 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 1715     41.41   
##  Residual             2618     51.17   
## Number of obs: 10557, groups:  ID, 7363
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             354.59172    8.99882
## timefactor2                                              -3.37673    2.03446
## timefactor3                                             -22.46835    2.10939
## PandemicFU2 data collected before COVID-19               -4.82709    1.60827
## Age                                                      -3.32451    0.08533
## SexM                                                     21.39900    1.58053
## EducationHigh School Diploma                             -0.05411    2.27081
## EducationLess than High School Diploma                    5.24321    3.27150
## EducationSome College                                     8.73991    2.77199
## EthnicityWhite                                            3.04163    4.38339
## IncomeLevel>$150k                                        37.94421    4.04081
## IncomeLevel$100-150k                                     39.03115    3.30693
## IncomeLevel$20-50k                                       18.54593    2.25777
## IncomeLevel$50-100k                                      27.18483    2.40637
## BMI                                                      -0.72893    0.15382
## CESD.20.1                                                -0.51776    0.18125
## SmokingStatusFormer Smoker                                1.62104    3.09435
## SmokingStatusNever Smoked                                 7.96421    3.21572
## SmokingStatusOccasional Smoker                            2.36244    6.08465
## RelationshipstatusMarried                                -9.44065    2.62506
## RelationshipstatusSeparated                              -1.61135    5.11007
## RelationshipstatusSingle                                -19.33857    3.57448
## RelationshipstatusWidowed                                -6.53039    3.65882
## LivingstatusAssisted Living                               1.87228   11.30287
## LivingstatusHouse                                        25.01274    2.38592
## LivingstatusOther                                        21.37458    8.95714
## AnxietyYes                                               -7.38451    3.14457
## MoodDisordYes                                            -9.07532    2.26153
## Chronicconditions                                        -1.71401    0.36873
## timefactor2:PandemicFU2 data collected before COVID-19   -3.52538    3.12451
## timefactor3:PandemicFU2 data collected before COVID-19    5.42835    2.98792
##                                                                df t value
## (Intercept)                                            7156.61916  39.404
## timefactor2                                            5779.91261  -1.660
## timefactor3                                            5780.37350 -10.652
## PandemicFU2 data collected before COVID-19             9646.34237  -3.001
## Age                                                    7013.83415 -38.958
## SexM                                                   6950.60590  13.539
## EducationHigh School Diploma                           7197.12755  -0.024
## EducationLess than High School Diploma                 7679.22551   1.603
## EducationSome College                                  6797.73017   3.153
## EthnicityWhite                                         7052.93348   0.694
## IncomeLevel>$150k                                      6799.41747   9.390
## IncomeLevel$100-150k                                   6869.59780  11.803
## IncomeLevel$20-50k                                     7150.39809   8.214
## IncomeLevel$50-100k                                    7043.76907  11.297
## BMI                                                    7386.87540  -4.739
## CESD.20.1                                              7064.35971  -2.857
## SmokingStatusFormer Smoker                             7299.56020   0.524
## SmokingStatusNever Smoked                              7278.66498   2.477
## SmokingStatusOccasional Smoker                         6756.91093   0.388
## RelationshipstatusMarried                              6895.83633  -3.596
## RelationshipstatusSeparated                            6989.58912  -0.315
## RelationshipstatusSingle                               6923.77551  -5.410
## RelationshipstatusWidowed                              7044.98777  -1.785
## LivingstatusAssisted Living                            6756.19138   0.166
## LivingstatusHouse                                      6875.22038  10.483
## LivingstatusOther                                      7411.75562   2.386
## AnxietyYes                                             7047.21420  -2.348
## MoodDisordYes                                          6936.59744  -4.013
## Chronicconditions                                      6897.43328  -4.648
## timefactor2:PandemicFU2 data collected before COVID-19 5729.99822  -1.128
## timefactor3:PandemicFU2 data collected before COVID-19 5685.85129   1.817
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            0.097015 .  
## timefactor3                                             < 2e-16 ***
## PandemicFU2 data collected before COVID-19             0.002694 ** 
## Age                                                     < 2e-16 ***
## SexM                                                    < 2e-16 ***
## EducationHigh School Diploma                           0.980990    
## EducationLess than High School Diploma                 0.109043    
## EducationSome College                                  0.001623 ** 
## EthnicityWhite                                         0.487769    
## IncomeLevel>$150k                                       < 2e-16 ***
## IncomeLevel$100-150k                                    < 2e-16 ***
## IncomeLevel$20-50k                                     2.51e-16 ***
## IncomeLevel$50-100k                                     < 2e-16 ***
## BMI                                                    2.19e-06 ***
## CESD.20.1                                              0.004296 ** 
## SmokingStatusFormer Smoker                             0.600384    
## SmokingStatusNever Smoked                              0.013285 *  
## SmokingStatusOccasional Smoker                         0.697834    
## RelationshipstatusMarried                              0.000325 ***
## RelationshipstatusSeparated                            0.752522    
## RelationshipstatusSingle                               6.51e-08 ***
## RelationshipstatusWidowed                              0.074331 .  
## LivingstatusAssisted Living                            0.868440    
## LivingstatusHouse                                       < 2e-16 ***
## LivingstatusOther                                      0.017043 *  
## AnxietyYes                                             0.018885 *  
## MoodDisordYes                                          6.06e-05 ***
## Chronicconditions                                      3.41e-06 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.259241    
## timefactor3:PandemicFU2 data collected before COVID-19 0.069305 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.7.1.2) Estimated marginal means

lsmeans(modelPASE_5, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  166.1802 4.882968 Inf  156.6098  175.7507
##  FU2 data collected before COVID-19 161.3531 4.946697 Inf  151.6578  171.0485
## 
## timefactor = 2:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  162.8035 5.165208 Inf  152.6799  172.9271
##  FU2 data collected before COVID-19 154.4510 5.318386 Inf  144.0272  164.8749
## 
## timefactor = 3:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  143.7119 5.187923 Inf  133.5437  153.8800
##  FU2 data collected before COVID-19 144.3132 5.214719 Inf  134.0925  154.5338
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelPASE_5, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df z.ratio p.value
##   4.827089 1.608266 Inf   3.001  0.0027
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df z.ratio p.value
##   8.352467 3.024190 Inf   2.762  0.0057
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df z.ratio p.value
##  -0.601265 2.882554 Inf  -0.209  0.8348
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelPASE_5, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df asymp.LCL asymp.UCL
##   4.827089 1.608266 Inf  1.674945  7.979233
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df asymp.LCL asymp.UCL
##   8.352467 3.024190 Inf  2.425164 14.279771
## 
## timefactor = 3:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df asymp.LCL asymp.UCL
##  -0.601265 2.882554 Inf -6.250967  5.048437
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.7.1.3) Graph of estimated marginal means

PASE_lsmeans_5 <- summary(lsmeans(modelPASE_5, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.

PASE_lsmeans_5$Time<-NA
PASE_lsmeans_5$Time[PASE_lsmeans_5$timefactor==1]<-"Baseline"
PASE_lsmeans_5$Time[PASE_lsmeans_5$timefactor==2]<-"Follow-up 1"
PASE_lsmeans_5$Time[PASE_lsmeans_5$timefactor==3]<-"Follow-up 2"
ggplot(PASE_lsmeans_5, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + 
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

16.7.2) Full model adjusted for baseline

16.7.2.1) Model

modelPASE_6<- lmer(PASE_TOTAL ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= truncated.data_long_2)
summary(modelPASE_6)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PASE_TOTAL ~ timefactor * Pandemic + Age + Sex + Education +  
##     Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +  
##     Relationshipstatus + Livingstatus + Anxiety + MoodDisord +  
##     Chronicconditions + PASE_TOTALbaseline + (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 34098.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6805 -0.5406 -0.0438  0.5107  4.5754 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 1216     34.87   
##  Residual             2223     47.15   
## Number of obs: 3125, groups:  ID, 2543
## 
## Fixed effects:
##                                                          Estimate Std. Error
## (Intercept)                                             232.73128   15.86106
## timefactor2                                             -19.62725    2.64845
## PandemicFU2 data collected before COVID-19               -5.30365    3.06354
## Age                                                      -2.36897    0.14780
## SexM                                                     18.17102    2.52075
## EducationHigh School Diploma                            -11.08048    3.74359
## EducationLess than High School Diploma                   -8.20565    6.08434
## EducationSome College                                     5.41348    4.24920
## EthnicityWhite                                           11.18200    7.07555
## IncomeLevel>$150k                                        11.54903    6.28360
## IncomeLevel$100-150k                                     13.26436    5.18297
## IncomeLevel$20-50k                                        6.06846    3.73566
## IncomeLevel$50-100k                                       9.01259    3.90698
## BMI                                                      -0.50866    0.26619
## CESD.10baseline                                           0.29557    0.29246
## SmokingStatusFormer Smoker                                4.30350    5.28360
## SmokingStatusNever Smoked                                 7.29594    5.46236
## SmokingStatusOccasional Smoker                            0.86889    9.28644
## RelationshipstatusMarried                                -3.04107    4.06275
## RelationshipstatusSeparated                               9.25215    8.04699
## RelationshipstatusSingle                                 -6.55354    5.61456
## RelationshipstatusWidowed                                -0.69439    5.87389
## LivingstatusAssisted Living                             -13.41829   17.10814
## LivingstatusHouse                                        13.36191    3.74568
## LivingstatusOther                                        37.92922   15.38442
## AnxietyYes                                               -3.58410    5.05293
## MoodDisordYes                                           -11.36014    3.55337
## Chronicconditions                                        -1.63359    0.57824
## PASE_TOTALbaseline                                        0.36106    0.01686
## timefactor2:PandemicFU2 data collected before COVID-19    9.13538    3.86024
##                                                                df t value
## (Intercept)                                            2433.40877  14.673
## timefactor2                                            1845.93403  -7.411
## PandemicFU2 data collected before COVID-19             3079.70469  -1.731
## Age                                                    2437.60274 -16.028
## SexM                                                   2423.85638   7.209
## EducationHigh School Diploma                           2429.98070  -2.960
## EducationLess than High School Diploma                 2440.54924  -1.349
## EducationSome College                                  2338.14352   1.274
## EthnicityWhite                                         2585.73345   1.580
## IncomeLevel>$150k                                      2381.36205   1.838
## IncomeLevel$100-150k                                   2437.66731   2.559
## IncomeLevel$20-50k                                     2373.86541   1.624
## IncomeLevel$50-100k                                    2376.39493   2.307
## BMI                                                    2435.29043  -1.911
## CESD.10baseline                                        2398.83981   1.011
## SmokingStatusFormer Smoker                             2359.64679   0.815
## SmokingStatusNever Smoked                              2358.57043   1.336
## SmokingStatusOccasional Smoker                         2330.59744   0.094
## RelationshipstatusMarried                              2463.12175  -0.749
## RelationshipstatusSeparated                            2362.19214   1.150
## RelationshipstatusSingle                               2355.45363  -1.167
## RelationshipstatusWidowed                              2401.80448  -0.118
## LivingstatusAssisted Living                            2279.55813  -0.784
## LivingstatusHouse                                      2423.26416   3.567
## LivingstatusOther                                      2414.59723   2.465
## AnxietyYes                                             2382.38859  -0.709
## MoodDisordYes                                          2437.12530  -3.197
## Chronicconditions                                      2361.78407  -2.825
## PASE_TOTALbaseline                                     2398.87470  21.419
## timefactor2:PandemicFU2 data collected before COVID-19 1763.51326   2.367
##                                                        Pr(>|t|)    
## (Intercept)                                             < 2e-16 ***
## timefactor2                                            1.90e-13 ***
## PandemicFU2 data collected before COVID-19             0.083513 .  
## Age                                                     < 2e-16 ***
## SexM                                                   7.52e-13 ***
## EducationHigh School Diploma                           0.003108 ** 
## EducationLess than High School Diploma                 0.177574    
## EducationSome College                                  0.202790    
## EthnicityWhite                                         0.114144    
## IncomeLevel>$150k                                      0.066192 .  
## IncomeLevel$100-150k                                   0.010551 *  
## IncomeLevel$20-50k                                     0.104409    
## IncomeLevel$50-100k                                    0.021152 *  
## BMI                                                    0.056135 .  
## CESD.10baseline                                        0.312300    
## SmokingStatusFormer Smoker                             0.415439    
## SmokingStatusNever Smoked                              0.181784    
## SmokingStatusOccasional Smoker                         0.925462    
## RelationshipstatusMarried                              0.454215    
## RelationshipstatusSeparated                            0.250357    
## RelationshipstatusSingle                               0.243231    
## RelationshipstatusWidowed                              0.905906    
## LivingstatusAssisted Living                            0.432933    
## LivingstatusHouse                                      0.000368 ***
## LivingstatusOther                                      0.013754 *  
## AnxietyYes                                             0.478201    
## MoodDisordYes                                          0.001406 ** 
## Chronicconditions                                      0.004766 ** 
## PASE_TOTALbaseline                                      < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.018063 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.7.2.2) Estimated marginal means

lsmeans(modelPASE_6, ~Pandemic|timefactor)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  162.9041 8.109685 Inf  147.0094  178.7988
##  FU2 data collected before COVID-19 157.6005 8.156860 Inf  141.6133  173.5876
## 
## timefactor = 2:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  143.2769 8.068424 Inf  127.4631  159.0907
##  FU2 data collected before COVID-19 147.1086 8.127680 Inf  131.1787  163.0386
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelPASE_6, ~Pandemic|timefactor), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df z.ratio p.value
##   5.303654 3.063540 Inf   1.731  0.0834
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df z.ratio p.value
##  -3.831727 2.919848 Inf  -1.312  0.1894
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelPASE_6, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df asymp.LCL asymp.UCL
##   5.303654 3.063540 Inf -0.700774 11.308082
## 
## timefactor = 2:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##   estimate       SE  df asymp.LCL asymp.UCL
##  -3.831727 2.919848 Inf -9.554524  1.891069
## 
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.7.2.3) Graph of estimated marginal means

PASE_lsmeans_6 <- summary(lsmeans(modelPASE_6, ~timefactor|Pandemic))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.

PASE_lsmeans_6$Time<-NA
PASE_lsmeans_6$Time[PASE_lsmeans_6$timefactor==1]<-"Follow-up 1"
PASE_lsmeans_6$Time[PASE_lsmeans_6$timefactor==2]<-"Follow-up 2"
ggplot(PASE_lsmeans_6, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

16.7.3) Full model unadjusted for baseline (age and sex differences)

16.7.3.1) Model

modelPASE_7<- lmer(PASE_TOTAL ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
                    (1|ID), data= truncated.data_long)
summary(modelPASE_7)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## PASE_TOTAL ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +  
##     IncomeLevel + BMI + CESD.20.1 + SmokingStatus + Relationshipstatus +  
##     Livingstatus + Anxiety + MoodDisord + Chronicconditions +      (1 | ID)
##    Data: truncated.data_long
## 
## REML criterion at convergence: 118108.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4851 -0.5420 -0.0355  0.4895  6.0021 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 2055     45.33   
##  Residual             2589     50.88   
## Number of obs: 10557, groups:  ID, 7363
## 
## Fixed effects:
##                                                                            Estimate
## (Intercept)                                                                170.4204
## timefactor2                                                                 -0.6545
## timefactor3                                                                -26.4561
## PandemicFU2 data collected before COVID-19                                  -7.6101
## Age_sexFemales 65+                                                         -45.9418
## Age_sexMales 45-64                                                          24.2064
## Age_sexMales 65+                                                           -41.9239
## EducationHigh School Diploma                                                -1.1925
## EducationLess than High School Diploma                                       1.1666
## EducationSome College                                                        5.9158
## EthnicityWhite                                                              -2.2806
## IncomeLevel>$150k                                                           42.9040
## IncomeLevel$100-150k                                                        44.7535
## IncomeLevel$20-50k                                                          19.4238
## IncomeLevel$50-100k                                                         30.8084
## BMI                                                                         -0.5189
## CESD.20.1                                                                   -0.3040
## SmokingStatusFormer Smoker                                                  -2.4601
## SmokingStatusNever Smoked                                                    5.0911
## SmokingStatusOccasional Smoker                                               1.4476
## RelationshipstatusMarried                                                   -7.5952
## RelationshipstatusSeparated                                                  2.8706
## RelationshipstatusSingle                                                   -15.9594
## RelationshipstatusWidowed                                                  -19.7036
## LivingstatusAssisted Living                                                -11.2470
## LivingstatusHouse                                                           28.7250
## LivingstatusOther                                                           26.3489
## AnxietyYes                                                                  -3.9435
## MoodDisordYes                                                               -8.1663
## Chronicconditions                                                           -3.1997
## timefactor2:PandemicFU2 data collected before COVID-19                      -7.4625
## timefactor3:PandemicFU2 data collected before COVID-19                       7.8750
## timefactor2:Age_sexFemales 65+                                             -10.0769
## timefactor3:Age_sexFemales 65+                                               0.4537
## timefactor2:Age_sexMales 45-64                                              -5.8609
## timefactor3:Age_sexMales 45-64                                               9.0115
## timefactor2:Age_sexMales 65+                                                 4.7942
## timefactor3:Age_sexMales 65+                                                -1.1259
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                5.0377
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               -6.5782
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 10.1817
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   11.6768
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    2.5661
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   12.3580
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   -8.9748
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    -10.1173
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      5.0424
##                                                                           Std. Error
## (Intercept)                                                                   7.8324
## timefactor2                                                                   3.6405
## timefactor3                                                                   3.7155
## PandemicFU2 data collected before COVID-19                                    2.8302
## Age_sexFemales 65+                                                            3.7711
## Age_sexMales 45-64                                                            2.7433
## Age_sexMales 65+                                                              3.4862
## EducationHigh School Diploma                                                  2.3696
## EducationLess than High School Diploma                                        3.4097
## EducationSome College                                                         2.8947
## EthnicityWhite                                                                4.5716
## IncomeLevel>$150k                                                             4.2171
## IncomeLevel$100-150k                                                          3.4454
## IncomeLevel$20-50k                                                            2.3586
## IncomeLevel$50-100k                                                           2.5116
## BMI                                                                           0.1602
## CESD.20.1                                                                     0.1890
## SmokingStatusFormer Smoker                                                    3.2238
## SmokingStatusNever Smoked                                                     3.3555
## SmokingStatusOccasional Smoker                                                6.3564
## RelationshipstatusMarried                                                     2.7474
## RelationshipstatusSeparated                                                   5.3341
## RelationshipstatusSingle                                                      3.7334
## RelationshipstatusWidowed                                                     3.8063
## LivingstatusAssisted Living                                                  11.8066
## LivingstatusHouse                                                             2.4908
## LivingstatusOther                                                             9.3461
## AnxietyYes                                                                    3.2799
## MoodDisordYes                                                                 2.3636
## Chronicconditions                                                             0.3811
## timefactor2:PandemicFU2 data collected before COVID-19                        5.2293
## timefactor3:PandemicFU2 data collected before COVID-19                        4.9455
## timefactor2:Age_sexFemales 65+                                                6.7449
## timefactor3:Age_sexFemales 65+                                                6.9969
## timefactor2:Age_sexMales 45-64                                                4.8875
## timefactor3:Age_sexMales 45-64                                                4.9782
## timefactor2:Age_sexMales 65+                                                  6.2795
## timefactor3:Age_sexMales 65+                                                  6.8235
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                 4.9332
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                 4.1119
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                   4.8518
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+     9.4198
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+     9.1762
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64     7.8248
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64     7.4449
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+       9.4964
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+       9.2345
##                                                                                  df
## (Intercept)                                                               7461.1624
## timefactor2                                                               5421.5375
## timefactor3                                                               5386.2216
## PandemicFU2 data collected before COVID-19                                9593.6178
## Age_sexFemales 65+                                                        9374.7818
## Age_sexMales 45-64                                                        9527.0386
## Age_sexMales 65+                                                          9445.5375
## EducationHigh School Diploma                                              7299.8641
## EducationLess than High School Diploma                                    7711.0961
## EducationSome College                                                     6939.3583
## EthnicityWhite                                                            7159.0380
## IncomeLevel>$150k                                                         6938.2991
## IncomeLevel$100-150k                                                      6998.5271
## IncomeLevel$20-50k                                                        7251.3329
## IncomeLevel$50-100k                                                       7162.7393
## BMI                                                                       7472.1519
## CESD.20.1                                                                 7181.1546
## SmokingStatusFormer Smoker                                                7389.4401
## SmokingStatusNever Smoked                                                 7369.3219
## SmokingStatusOccasional Smoker                                            6902.4387
## RelationshipstatusMarried                                                 7043.8296
## RelationshipstatusSeparated                                               7111.4771
## RelationshipstatusSingle                                                  7064.2457
## RelationshipstatusWidowed                                                 7156.1943
## LivingstatusAssisted Living                                               6905.9426
## LivingstatusHouse                                                         7007.5093
## LivingstatusOther                                                         7497.6452
## AnxietyYes                                                                7163.4535
## MoodDisordYes                                                             7061.4096
## Chronicconditions                                                         7053.3850
## timefactor2:PandemicFU2 data collected before COVID-19                    5429.0438
## timefactor3:PandemicFU2 data collected before COVID-19                    5297.6457
## timefactor2:Age_sexFemales 65+                                            5620.7590
## timefactor3:Age_sexFemales 65+                                            5728.6722
## timefactor2:Age_sexMales 45-64                                            5531.8559
## timefactor3:Age_sexMales 45-64                                            5511.4953
## timefactor2:Age_sexMales 65+                                              5431.3158
## timefactor3:Age_sexMales 65+                                              5447.5955
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             9595.7201
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             9583.3879
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               9536.6153
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 5542.7462
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 5613.7398
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 5473.7923
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 5413.5081
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   5455.5728
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   5433.4943
##                                                                           t value
## (Intercept)                                                                21.758
## timefactor2                                                                -0.180
## timefactor3                                                                -7.121
## PandemicFU2 data collected before COVID-19                                 -2.689
## Age_sexFemales 65+                                                        -12.183
## Age_sexMales 45-64                                                          8.824
## Age_sexMales 65+                                                          -12.026
## EducationHigh School Diploma                                               -0.503
## EducationLess than High School Diploma                                      0.342
## EducationSome College                                                       2.044
## EthnicityWhite                                                             -0.499
## IncomeLevel>$150k                                                          10.174
## IncomeLevel$100-150k                                                       12.989
## IncomeLevel$20-50k                                                          8.235
## IncomeLevel$50-100k                                                        12.266
## BMI                                                                        -3.239
## CESD.20.1                                                                  -1.608
## SmokingStatusFormer Smoker                                                 -0.763
## SmokingStatusNever Smoked                                                   1.517
## SmokingStatusOccasional Smoker                                              0.228
## RelationshipstatusMarried                                                  -2.765
## RelationshipstatusSeparated                                                 0.538
## RelationshipstatusSingle                                                   -4.275
## RelationshipstatusWidowed                                                  -5.177
## LivingstatusAssisted Living                                                -0.953
## LivingstatusHouse                                                          11.532
## LivingstatusOther                                                           2.819
## AnxietyYes                                                                 -1.202
## MoodDisordYes                                                              -3.455
## Chronicconditions                                                          -8.397
## timefactor2:PandemicFU2 data collected before COVID-19                     -1.427
## timefactor3:PandemicFU2 data collected before COVID-19                      1.592
## timefactor2:Age_sexFemales 65+                                             -1.494
## timefactor3:Age_sexFemales 65+                                              0.065
## timefactor2:Age_sexMales 45-64                                             -1.199
## timefactor3:Age_sexMales 45-64                                              1.810
## timefactor2:Age_sexMales 65+                                                0.763
## timefactor3:Age_sexMales 65+                                               -0.165
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               1.021
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              -1.600
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 2.099
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   1.240
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   0.280
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64   1.579
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -1.206
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    -1.065
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     0.546
##                                                                           Pr(>|t|)
## (Intercept)                                                                < 2e-16
## timefactor2                                                               0.857334
## timefactor3                                                               1.22e-12
## PandemicFU2 data collected before COVID-19                                0.007182
## Age_sexFemales 65+                                                         < 2e-16
## Age_sexMales 45-64                                                         < 2e-16
## Age_sexMales 65+                                                           < 2e-16
## EducationHigh School Diploma                                              0.614802
## EducationLess than High School Diploma                                    0.732250
## EducationSome College                                                     0.041026
## EthnicityWhite                                                            0.617896
## IncomeLevel>$150k                                                          < 2e-16
## IncomeLevel$100-150k                                                       < 2e-16
## IncomeLevel$20-50k                                                         < 2e-16
## IncomeLevel$50-100k                                                        < 2e-16
## BMI                                                                       0.001203
## CESD.20.1                                                                 0.107863
## SmokingStatusFormer Smoker                                                0.445423
## SmokingStatusNever Smoked                                                 0.129240
## SmokingStatusOccasional Smoker                                            0.819854
## RelationshipstatusMarried                                                 0.005715
## RelationshipstatusSeparated                                               0.590476
## RelationshipstatusSingle                                                  1.94e-05
## RelationshipstatusWidowed                                                 2.32e-07
## LivingstatusAssisted Living                                               0.340824
## LivingstatusHouse                                                          < 2e-16
## LivingstatusOther                                                         0.004826
## AnxietyYes                                                                0.229282
## MoodDisordYes                                                             0.000554
## Chronicconditions                                                          < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                    0.153625
## timefactor3:PandemicFU2 data collected before COVID-19                    0.111366
## timefactor2:Age_sexFemales 65+                                            0.135232
## timefactor3:Age_sexFemales 65+                                            0.948300
## timefactor2:Age_sexMales 45-64                                            0.230515
## timefactor3:Age_sexMales 45-64                                            0.070322
## timefactor2:Age_sexMales 65+                                              0.445211
## timefactor3:Age_sexMales 65+                                              0.868944
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             0.307192
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             0.109675
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               0.035884
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.215176
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.779761
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.114317
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.228063
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.286746
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   0.585061
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                                  
## timefactor3                                                               ***
## PandemicFU2 data collected before COVID-19                                ** 
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                        ***
## Age_sexMales 65+                                                          ***
## EducationHigh School Diploma                                                 
## EducationLess than High School Diploma                                       
## EducationSome College                                                     *  
## EthnicityWhite                                                               
## IncomeLevel>$150k                                                         ***
## IncomeLevel$100-150k                                                      ***
## IncomeLevel$20-50k                                                        ***
## IncomeLevel$50-100k                                                       ***
## BMI                                                                       ** 
## CESD.20.1                                                                    
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                 ** 
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                  ***
## RelationshipstatusWidowed                                                 ***
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         ***
## LivingstatusOther                                                         ** 
## AnxietyYes                                                                   
## MoodDisordYes                                                             ***
## Chronicconditions                                                         ***
## timefactor2:PandemicFU2 data collected before COVID-19                       
## timefactor3:PandemicFU2 data collected before COVID-19                       
## timefactor2:Age_sexFemales 65+                                               
## timefactor3:Age_sexFemales 65+                                               
## timefactor2:Age_sexMales 45-64                                               
## timefactor3:Age_sexMales 45-64                                            .  
## timefactor2:Age_sexMales 65+                                                 
## timefactor3:Age_sexMales 65+                                                 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               *  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 47 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.7.3.2) Estimated marginal means

lsmeans(modelPASE_7, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  172.3904 5.418116 Inf 161.77110  183.0097
##  FU2 data collected before COVID-19 164.7803 5.419626 Inf 154.15802  175.4026
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  171.7359 6.134535 Inf 159.71247  183.7594
##  FU2 data collected before COVID-19 156.6633 6.225348 Inf 144.46186  168.8648
## 
## timefactor = 3, Age_sex = Females 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  145.9343 6.195971 Inf 133.79040  158.0782
##  FU2 data collected before COVID-19 146.1992 5.964233 Inf 134.50947  157.8888
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  126.4487 5.797846 Inf 115.08508  137.8122
##  FU2 data collected before COVID-19 123.8763 5.818754 Inf 112.47172  135.2808
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  115.7173 7.492599 Inf 101.03204  130.4025
##  FU2 data collected before COVID-19 117.3592 7.314920 Inf 103.02221  131.6962
## 
## timefactor = 3, Age_sex = Females 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  100.4462 7.585409 Inf  85.57911  115.3134
##  FU2 data collected before COVID-19 108.3149 6.998143 Inf  94.59880  122.0310
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  196.5968 5.267884 Inf 186.27193  206.9217
##  FU2 data collected before COVID-19 182.4085 5.567387 Inf 171.49661  193.3204
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  190.0815 5.907899 Inf 178.50219  201.6607
##  FU2 data collected before COVID-19 180.7886 6.865830 Inf 167.33182  194.2454
## 
## timefactor = 3, Age_sex = Males 45-64:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  179.1521 5.942304 Inf 167.50544  190.7988
##  FU2 data collected before COVID-19 163.8640 6.636062 Inf 150.85759  176.8705
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  130.4665 5.782253 Inf 119.13346  141.7995
##  FU2 data collected before COVID-19 133.0380 5.798126 Inf 121.67393  144.4022
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  134.6062 7.134851 Inf 120.62218  148.5903
##  FU2 data collected before COVID-19 119.5980 7.803670 Inf 104.30308  134.8929
## 
## timefactor = 3, Age_sex = Males 65+:
##  Pandemic                             lsmean       SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  102.8844 7.596903 Inf  87.99476  117.7741
##  FU2 data collected before COVID-19 118.3734 7.173194 Inf 104.31419  132.4326
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelPASE_7, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##    7.610119 2.830240 Inf   2.689  0.0072
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   15.072620 5.070876 Inf   2.972  0.0030
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   -0.264873 4.789260 Inf  -0.055  0.9559
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##    2.572386 4.051453 Inf   0.635  0.5255
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   -1.641929 7.622946 Inf  -0.215  0.8295
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   -7.868669 7.492699 Inf  -1.050  0.2936
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   14.188308 2.989910 Inf   4.745  <.0001
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##    9.292856 5.672901 Inf   1.638  0.1014
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   15.288108 5.407340 Inf   2.827  0.0047
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   -2.571581 3.947393 Inf  -0.651  0.5147
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   15.008245 7.756102 Inf   1.935  0.0530
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##  -15.488976 7.617879 Inf  -2.033  0.0420
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelPASE_7, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##    7.610119 2.830240 Inf   2.062950 13.157288
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##   15.072620 5.070876 Inf   5.133886 25.011354
## 
## timefactor = 3, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##   -0.264873 4.789260 Inf  -9.651650  9.121904
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##    2.572386 4.051453 Inf  -5.368316 10.513087
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##   -1.641929 7.622946 Inf -16.582628 13.298771
## 
## timefactor = 3, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##   -7.868669 7.492699 Inf -22.554089  6.816750
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##   14.188308 2.989910 Inf   8.328191 20.048424
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##    9.292856 5.672901 Inf  -1.825824 20.411537
## 
## timefactor = 3, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##   15.288108 5.407340 Inf   4.689915 25.886300
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##   -2.571581 3.947393 Inf -10.308330  5.165168
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##   15.008245 7.756102 Inf  -0.193437 30.209926
## 
## timefactor = 3, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##  -15.488976 7.617879 Inf -30.419745 -0.558206
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.7.3.3) Graph of estimated marginal means

PASE_lsmeans_7 <- summary(lsmeans(modelPASE_7, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.

PASE_lsmeans_7$Time<-NA
PASE_lsmeans_7$Time[PASE_lsmeans_7$timefactor==1]<-"Baseline"
PASE_lsmeans_7$Time[PASE_lsmeans_7$timefactor==2]<-"Follow-up 1"
PASE_lsmeans_7$Time[PASE_lsmeans_7$timefactor==3]<-"Follow-up 2"
ggplot(PASE_lsmeans_7, aes(x = Time, y = lsmean, group=Pandemic)) +
  geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
  geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
  geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
  labs(x = "Time", y = "MAT Score", title = "PASE Total Score from Baseline to FU2 by Pandemic status") +
  theme_bw()

16.7.4) Full model adjusted for baseline (age and sex differences)

16.7.4.1) Model

modelPASE_8<- lmer(PASE_TOTAL ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
                         Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
                        (1|ID), data= truncated.data_long_2)
summary(modelPASE_8)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## PASE_TOTAL ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +  
##     IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +  
##     Livingstatus + Anxiety + MoodDisord + Chronicconditions +  
##     PASE_TOTALbaseline + (1 | ID)
##    Data: truncated.data_long_2
## 
## REML criterion at convergence: 34149.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5698 -0.5427 -0.0392  0.4979  4.0877 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  ID       (Intercept) 1398     37.39   
##  Residual             2202     46.92   
## Number of obs: 3125, groups:  ID, 2543
## 
## Fixed effects:
##                                                                            Estimate
## (Intercept)                                                                 95.4964
## timefactor2                                                                -25.7490
## PandemicFU2 data collected before COVID-19                                  -8.8218
## Age_sexFemales 65+                                                         -35.2604
## Age_sexMales 45-64                                                          13.2060
## Age_sexMales 65+                                                           -18.0100
## EducationHigh School Diploma                                               -11.7484
## EducationLess than High School Diploma                                     -12.0805
## EducationSome College                                                        3.2351
## EthnicityWhite                                                               7.2713
## IncomeLevel>$150k                                                           12.7741
## IncomeLevel$100-150k                                                        14.9002
## IncomeLevel$20-50k                                                           4.6884
## IncomeLevel$50-100k                                                          9.2970
## BMI                                                                         -0.4072
## CESD.10baseline                                                              0.4619
## SmokingStatusFormer Smoker                                                   2.3891
## SmokingStatusNever Smoked                                                    6.3021
## SmokingStatusOccasional Smoker                                               3.0490
## RelationshipstatusMarried                                                   -1.8200
## RelationshipstatusSeparated                                                 11.7951
## RelationshipstatusSingle                                                    -3.8715
## RelationshipstatusWidowed                                                   -9.8022
## LivingstatusAssisted Living                                                -21.8178
## LivingstatusHouse                                                           15.5912
## LivingstatusOther                                                           41.5047
## AnxietyYes                                                                  -1.3635
## MoodDisordYes                                                              -11.0153
## Chronicconditions                                                           -2.5631
## PASE_TOTALbaseline                                                           0.4154
## timefactor2:PandemicFU2 data collected before COVID-19                      13.3645
## timefactor2:Age_sexFemales 65+                                              12.8453
## timefactor2:Age_sexMales 45-64                                              12.0538
## timefactor2:Age_sexMales 65+                                                -5.2585
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               11.3888
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                4.0639
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                 -7.7593
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+   -8.8063
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -14.4876
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     15.9599
##                                                                           Std. Error
## (Intercept)                                                                  13.2006
## timefactor2                                                                   4.6306
## PandemicFU2 data collected before COVID-19                                    5.1185
## Age_sexFemales 65+                                                            6.9763
## Age_sexMales 45-64                                                            4.8934
## Age_sexMales 65+                                                              6.2591
## EducationHigh School Diploma                                                  3.8518
## EducationLess than High School Diploma                                        6.2412
## EducationSome College                                                         4.3676
## EthnicityWhite                                                                7.2563
## IncomeLevel>$150k                                                             6.4611
## IncomeLevel$100-150k                                                          5.3259
## IncomeLevel$20-50k                                                            3.8403
## IncomeLevel$50-100k                                                           4.0208
## BMI                                                                           0.2734
## CESD.10baseline                                                               0.3006
## SmokingStatusFormer Smoker                                                    5.4325
## SmokingStatusNever Smoked                                                     5.6200
## SmokingStatusOccasional Smoker                                                9.5521
## RelationshipstatusMarried                                                     4.2028
## RelationshipstatusSeparated                                                   8.2761
## RelationshipstatusSingle                                                      5.7846
## RelationshipstatusWidowed                                                     6.0223
## LivingstatusAssisted Living                                                  17.6015
## LivingstatusHouse                                                             3.8523
## LivingstatusOther                                                            15.8426
## AnxietyYes                                                                    5.1909
## MoodDisordYes                                                                 3.6572
## Chronicconditions                                                             0.5931
## PASE_TOTALbaseline                                                            0.0166
## timefactor2:PandemicFU2 data collected before COVID-19                        6.3516
## timefactor2:Age_sexFemales 65+                                                8.8644
## timefactor2:Age_sexMales 45-64                                                6.2820
## timefactor2:Age_sexMales 65+                                                  8.3477
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                 9.2772
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                 7.6516
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                   9.2440
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    11.8532
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64     9.6114
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      11.8721
##                                                                                  df
## (Intercept)                                                               2550.9670
## timefactor2                                                               1661.3322
## PandemicFU2 data collected before COVID-19                                3047.3970
## Age_sexFemales 65+                                                        3084.8545
## Age_sexMales 45-64                                                        3083.8475
## Age_sexMales 65+                                                          3084.8802
## EducationHigh School Diploma                                              2448.8516
## EducationLess than High School Diploma                                    2454.2637
## EducationSome College                                                     2361.3771
## EthnicityWhite                                                            2585.0053
## IncomeLevel>$150k                                                         2402.6331
## IncomeLevel$100-150k                                                      2452.4249
## IncomeLevel$20-50k                                                        2389.6708
## IncomeLevel$50-100k                                                       2395.1996
## BMI                                                                       2452.6833
## CESD.10baseline                                                           2415.6237
## SmokingStatusFormer Smoker                                                2380.8832
## SmokingStatusNever Smoked                                                 2378.9700
## SmokingStatusOccasional Smoker                                            2356.0664
## RelationshipstatusMarried                                                 2479.2893
## RelationshipstatusSeparated                                               2382.8738
## RelationshipstatusSingle                                                  2380.2978
## RelationshipstatusWidowed                                                 2420.7702
## LivingstatusAssisted Living                                               2311.1037
## LivingstatusHouse                                                         2441.9050
## LivingstatusOther                                                         2437.9568
## AnxietyYes                                                                2402.2627
## MoodDisordYes                                                             2454.0552
## Chronicconditions                                                         2384.3109
## PASE_TOTALbaseline                                                        2420.2438
## timefactor2:PandemicFU2 data collected before COVID-19                    1587.7940
## timefactor2:Age_sexFemales 65+                                            1727.9138
## timefactor2:Age_sexMales 45-64                                            1769.4141
## timefactor2:Age_sexMales 65+                                              1847.3384
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+             3066.5688
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64             3057.8793
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+               3065.8730
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1685.9006
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1693.7893
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+   1826.7015
##                                                                           t value
## (Intercept)                                                                 7.234
## timefactor2                                                                -5.561
## PandemicFU2 data collected before COVID-19                                 -1.724
## Age_sexFemales 65+                                                         -5.054
## Age_sexMales 45-64                                                          2.699
## Age_sexMales 65+                                                           -2.877
## EducationHigh School Diploma                                               -3.050
## EducationLess than High School Diploma                                     -1.936
## EducationSome College                                                       0.741
## EthnicityWhite                                                              1.002
## IncomeLevel>$150k                                                           1.977
## IncomeLevel$100-150k                                                        2.798
## IncomeLevel$20-50k                                                          1.221
## IncomeLevel$50-100k                                                         2.312
## BMI                                                                        -1.489
## CESD.10baseline                                                             1.536
## SmokingStatusFormer Smoker                                                  0.440
## SmokingStatusNever Smoked                                                   1.121
## SmokingStatusOccasional Smoker                                              0.319
## RelationshipstatusMarried                                                  -0.433
## RelationshipstatusSeparated                                                 1.425
## RelationshipstatusSingle                                                   -0.669
## RelationshipstatusWidowed                                                  -1.628
## LivingstatusAssisted Living                                                -1.240
## LivingstatusHouse                                                           4.047
## LivingstatusOther                                                           2.620
## AnxietyYes                                                                 -0.263
## MoodDisordYes                                                              -3.012
## Chronicconditions                                                          -4.322
## PASE_TOTALbaseline                                                         25.029
## timefactor2:PandemicFU2 data collected before COVID-19                      2.104
## timefactor2:Age_sexFemales 65+                                              1.449
## timefactor2:Age_sexMales 45-64                                              1.919
## timefactor2:Age_sexMales 65+                                               -0.630
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+               1.228
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64               0.531
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                -0.839
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  -0.743
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  -1.507
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+     1.344
##                                                                           Pr(>|t|)
## (Intercept)                                                               6.16e-13
## timefactor2                                                               3.13e-08
## PandemicFU2 data collected before COVID-19                                 0.08490
## Age_sexFemales 65+                                                        4.57e-07
## Age_sexMales 45-64                                                         0.00700
## Age_sexMales 65+                                                           0.00404
## EducationHigh School Diploma                                               0.00231
## EducationLess than High School Diploma                                     0.05303
## EducationSome College                                                      0.45895
## EthnicityWhite                                                             0.31640
## IncomeLevel>$150k                                                          0.04815
## IncomeLevel$100-150k                                                       0.00519
## IncomeLevel$20-50k                                                         0.22227
## IncomeLevel$50-100k                                                        0.02085
## BMI                                                                        0.13659
## CESD.10baseline                                                            0.12456
## SmokingStatusFormer Smoker                                                 0.66013
## SmokingStatusNever Smoked                                                  0.26224
## SmokingStatusOccasional Smoker                                             0.74961
## RelationshipstatusMarried                                                  0.66503
## RelationshipstatusSeparated                                                0.15423
## RelationshipstatusSingle                                                   0.50339
## RelationshipstatusWidowed                                                  0.10373
## LivingstatusAssisted Living                                                0.21527
## LivingstatusHouse                                                         5.34e-05
## LivingstatusOther                                                          0.00885
## AnxietyYes                                                                 0.79283
## MoodDisordYes                                                              0.00262
## Chronicconditions                                                         1.61e-05
## PASE_TOTALbaseline                                                         < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19                     0.03553
## timefactor2:Age_sexFemales 65+                                             0.14749
## timefactor2:Age_sexMales 45-64                                             0.05517
## timefactor2:Age_sexMales 65+                                               0.52882
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+              0.21969
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64              0.59538
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                0.40132
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  0.45761
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  0.13191
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+    0.17901
##                                                                              
## (Intercept)                                                               ***
## timefactor2                                                               ***
## PandemicFU2 data collected before COVID-19                                .  
## Age_sexFemales 65+                                                        ***
## Age_sexMales 45-64                                                        ** 
## Age_sexMales 65+                                                          ** 
## EducationHigh School Diploma                                              ** 
## EducationLess than High School Diploma                                    .  
## EducationSome College                                                        
## EthnicityWhite                                                               
## IncomeLevel>$150k                                                         *  
## IncomeLevel$100-150k                                                      ** 
## IncomeLevel$20-50k                                                           
## IncomeLevel$50-100k                                                       *  
## BMI                                                                          
## CESD.10baseline                                                              
## SmokingStatusFormer Smoker                                                   
## SmokingStatusNever Smoked                                                    
## SmokingStatusOccasional Smoker                                               
## RelationshipstatusMarried                                                    
## RelationshipstatusSeparated                                                  
## RelationshipstatusSingle                                                     
## RelationshipstatusWidowed                                                    
## LivingstatusAssisted Living                                                  
## LivingstatusHouse                                                         ***
## LivingstatusOther                                                         ** 
## AnxietyYes                                                                   
## MoodDisordYes                                                             ** 
## Chronicconditions                                                         ***
## PASE_TOTALbaseline                                                        ***
## timefactor2:PandemicFU2 data collected before COVID-19                    *  
## timefactor2:Age_sexFemales 65+                                               
## timefactor2:Age_sexMales 45-64                                            .  
## timefactor2:Age_sexMales 65+                                                 
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+                
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64                
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+                  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64    
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 40 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

16.7.4.2) Estimated marginal means

lsmeans(modelPASE_8, ~Pandemic|timefactor|Age_sex)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  167.0874  8.864445 Inf 149.71343  184.4614
##  FU2 data collected before COVID-19 158.2656  8.868016 Inf 140.88466  175.6466
## 
## timefactor = 2, Age_sex = Females 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  141.3385  8.885093 Inf 123.92400  158.7529
##  FU2 data collected before COVID-19 145.8812  8.708717 Inf 128.81247  162.9500
## 
## timefactor = 1, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  131.8270  9.945986 Inf 112.33321  151.3208
##  FU2 data collected before COVID-19 134.3940  9.756196 Inf 115.27226  153.5159
## 
## timefactor = 2, Age_sex = Females 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  118.9233  9.906700 Inf  99.50656  138.3401
##  FU2 data collected before COVID-19 126.0486  9.563616 Inf 107.30428  144.7930
## 
## timefactor = 1, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  180.2934  8.737358 Inf 163.16850  197.4183
##  FU2 data collected before COVID-19 175.5355  9.323519 Inf 157.26175  193.8093
## 
## timefactor = 2, Age_sex = Males 45-64:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  166.5983  8.708821 Inf 149.52927  183.6672
##  FU2 data collected before COVID-19 160.7173  9.214853 Inf 142.65654  178.7781
## 
## timefactor = 1, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  149.0774  9.650794 Inf 130.16217  167.9926
##  FU2 data collected before COVID-19 132.4963 10.063831 Inf 112.77153  152.2210
## 
## timefactor = 2, Age_sex = Males 65+:
##  Pandemic                             lsmean        SE  df asymp.LCL asymp.UCL
##  FU2 data collected after COVID-19  118.0700  9.998764 Inf  98.47274  137.6672
##  FU2 data collected before COVID-19 130.8133  9.606062 Inf 111.98578  149.6409
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

contrast(lsmeans(modelPASE_8, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##    8.821770 5.118474 Inf   1.724  0.0848
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   -4.542776 4.783003 Inf  -0.950  0.3422
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   -2.567071 7.747155 Inf  -0.331  0.7404
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   -7.125282 7.744893 Inf  -0.920  0.3576
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##    4.757892 5.701255 Inf   0.835  0.4040
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##    5.880923 5.437575 Inf   1.082  0.2795
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##   16.581112 7.721617 Inf   2.147  0.0318
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df z.ratio p.value
##  -12.743366 7.617350 Inf  -1.673  0.0943
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic

confint(contrast(lsmeans(modelPASE_8, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.

## timefactor = 1, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##    8.821770 5.118474 Inf  -1.210254  18.85379
## 
## timefactor = 2, Age_sex = Females 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##   -4.542776 4.783003 Inf -13.917289   4.83174
## 
## timefactor = 1, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##   -2.567071 7.747155 Inf -17.751216  12.61707
## 
## timefactor = 2, Age_sex = Females 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##   -7.125282 7.744893 Inf -22.304992   8.05443
## 
## timefactor = 1, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##    4.757892 5.701255 Inf  -6.416363  15.93215
## 
## timefactor = 2, Age_sex = Males 45-64:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##    5.880923 5.437575 Inf  -4.776527  16.53837
## 
## timefactor = 1, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##   16.581112 7.721617 Inf   1.447022  31.71520
## 
## timefactor = 2, Age_sex = Males 65+:
##  contrast                                                                  
##  (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
##    estimate       SE  df  asymp.LCL asymp.UCL
##  -12.743366 7.617350 Inf -27.673097   2.18637
## 
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95

16.7.4.3) Graph of estimated marginal means

PASE_lsmeans_8 <- summary(lsmeans(modelPASE_8, ~timefactor|Pandemic|Age_sex))

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.

PASE_lsmeans_8$Time<-NA
PASE_lsmeans_8$Time[PASE_lsmeans_8$timefactor==1]<-"Follow-up 1"
PASE_lsmeans_8$Time[PASE_lsmeans_8$timefactor==2]<-"Follow-up 2"
ggplot(PASE_lsmeans_8, aes(x = Time, y = lsmean, fill = Pandemic)) +
  geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
  labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from FU1 to FU2 by Pandemic status
       (controlling for baseline)") +
  theme_bw()

16.8) Sedentary behaviour Results

16.8.1) Full model adjusted for baseline

Create binary variable for 4+ hrs/day of SB

truncated.data_long_2$SB.binary <- as.factor(ifelse(truncated.data_long_2$PASE_Sit==10, 1, 0))
truncated.data_long_2$SBbaseline.binary <- as.factor(ifelse(truncated.data_long_2$PASE_Sitbaseline==10, 1, 0))

16.8.1.1) Model

sit3 <- glmer(
  SB.binary ~ timefactor*Pandemic + Age + Sex + SBbaseline.binary + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = truncated.data_long_2, 
  family = binomial(link = "logit")
)

## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations

## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 5.82512 (tol = 0.002, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?

sit3

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: SB.binary ~ timefactor * Pandemic + Age + Sex + SBbaseline.binary +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: truncated.data_long_2
##       AIC       BIC    logLik  deviance  df.resid 
##  21003.89  21243.53 -10470.94  20941.89     16789 
## Random effects:
##  Groups Name        Std.Dev.
##  ID     (Intercept) 0.8871  
## Number of obs: 16820, groups:  ID, 8439
## Fixed Effects:
##                                            (Intercept)  
##                                               -2.43888  
##                                            timefactor2  
##                                                0.49340  
##             PandemicFU2 data collected before COVID-19  
##                                                0.01055  
##                                                    Age  
##                                                0.01880  
##                                                   SexM  
##                                               -0.13407  
##                                     SBbaseline.binary1  
##                                                1.10919  
##                           EducationHigh School Diploma  
##                                                0.02148  
##                 EducationLess than High School Diploma  
##                                                0.04818  
##                                  EducationSome College  
##                                               -0.04292  
##                                         EthnicityWhite  
##                                               -0.16834  
##                                      IncomeLevel>$150k  
##                                               -0.18740  
##                                   IncomeLevel$100-150k  
##                                                0.06256  
##                                     IncomeLevel$20-50k  
##                                               -0.17761  
##                                    IncomeLevel$50-100k  
##                                               -0.23650  
##                                                    BMI  
##                                                0.04201  
##                                        CESD.10baseline  
##                                                0.01536  
##                             SmokingStatusFormer Smoker  
##                                               -0.33779  
##                              SmokingStatusNever Smoked  
##                                               -0.33952  
##                         SmokingStatusOccasional Smoker  
##                                               -0.28201  
##                              RelationshipstatusMarried  
##                                               -0.20546  
##                            RelationshipstatusSeparated  
##                                               -0.05836  
##                               RelationshipstatusSingle  
##                                                0.04569  
##                              RelationshipstatusWidowed  
##                                               -0.03352  
##                            LivingstatusAssisted Living  
##                                                0.10082  
##                                      LivingstatusHouse  
##                                               -0.28383  
##                                      LivingstatusOther  
##                                               -0.63877  
##                                             AnxietyYes  
##                                                0.01032  
##                                          MoodDisordYes  
##                                                0.07900  
##                                      Chronicconditions  
##                                                0.02677  
## timefactor2:PandemicFU2 data collected before COVID-19  
##                                               -0.25441  
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 3 lme4 warnings

16.8.1.2) Predicted probabilities

ggpredict(sit3, c("timefactor", "Pandemic"))

## # Predicted probabilities of SB.binary
## 
## # Pandemic = FU2 data collected after COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.50 | [0.41, 0.58]
## 2          |      0.62 | [0.53, 0.70]
## 
## # Pandemic = FU2 data collected before COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.50 | [0.42, 0.59]
## 2          |      0.56 | [0.47, 0.64]
## 
## Adjusted for:
## *                Age =                    60.00
## *                Sex =                        F
## *  SBbaseline.binary =                        0
## *          Education = College Degree or Higher
## *          Ethnicity =                    Other
## *        IncomeLevel =                    <$20k
## *                BMI =                    27.50
## *    CESD.10baseline =                     4.88
## *      SmokingStatus =             Daily Smoker
## * Relationshipstatus =                 Divorced
## *       Livingstatus = Apartment/Condo/Townhome
## *            Anxiety =                       No
## *         MoodDisord =                       No
## *  Chronicconditions =                     2.71
## *                 ID =     0 (population-level)

16.8.1.3) Contrasts for predicted probilities

Mean Differences and 95% CIs

sit3.test <- as.data.frame(ggpredict(sit3, c("timefactor", "Pandemic")))

mean.diff.5<-(subset(sit3.test,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit3.test,x==1 & group == "FU2 data collected before COVID-19")$predicted)
         
se.5<-(sqrt(((subset(sit3.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
         (subset(sit3.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))

UL.5<-mean.diff.5 + se.5*1.96
LL.5<-mean.diff.5 - se.5*1.96

         
mean.diff.6<-(subset(sit3.test,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit3.test,x==2 & group == "FU2 data collected before COVID-19")$predicted)
         
se.6<-(sqrt(((subset(sit3.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
         (subset(sit3.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))

UL.6<-mean.diff.6 + se.6*1.96
LL.6<-mean.diff.6 - se.6*1.96

#Mean differences and LL and UL
mean.diff.5

## [1] -0.002636852

UL.5

## [1] 0.3433034

LL.5

## [1] -0.3485771

mean.diff.6

## [1] 0.05892594

UL.6

## [1] 0.405127

LL.6

## [1] -0.2872752

Z-Scores

sit3.test <- as.data.frame(ggpredict(sit3, c("timefactor", "Pandemic")))

z.5<- (subset(sit3.test,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit3.test,x==1 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sit3.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
     (subset(sit3.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
     
z.6<- (subset(sit3.test,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sit3.test,x==2 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sit3.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
         (subset(sit3.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))

#z-scores
z.5

## [1] -0.01493966

z.6

## [1] 0.3336062

p-values for z-scores

2*pnorm(z.5, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 1.01192

2*pnorm(z.6, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.7386767

16.8.1.4) Graph of predicted probabilities

ggpredict(sit3, c("timefactor", "Pandemic")) %>% plot()

16.8.2) Full model adjusted for baseline (age and sex differences)

16.8.2.1) Model

sit4 <- glmer(
  SB.binary ~ timefactor*Pandemic*Age_sex + SBbaseline.binary + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = truncated.data_long_2, 
  family = binomial(link = "logit")
)

## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations

## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.229753 (tol = 0.002, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

sit2

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: SB.binary ~ timefactor * Pandemic * Age_sex + SBbaseline.binary +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: Tracking.data_long_2
##       AIC       BIC    logLik  deviance  df.resid 
##  25497.29  25822.13 -12707.64  25415.29     20352 
## Random effects:
##  Groups Name        Std.Dev.
##  ID     (Intercept) 0.901   
## Number of obs: 20393, groups:  ID, 10230
## Fixed Effects:
##                                                               (Intercept)  
##                                                                 -1.620686  
##                                                               timefactor2  
##                                                                  0.580652  
##                                PandemicFU2 data collected before COVID-19  
##                                                                  0.057124  
##                                                        Age_sexFemales 65+  
##                                                                  0.398934  
##                                                        Age_sexMales 45-64  
##                                                                 -0.146265  
##                                                          Age_sexMales 65+  
##                                                                  0.303371  
##                                                        SBbaseline.binary1  
##                                                                  1.150707  
##                                              EducationHigh School Diploma  
##                                                                 -0.005042  
##                                    EducationLess than High School Diploma  
##                                                                  0.096709  
##                                                     EducationSome College  
##                                                                 -0.073105  
##                                                            EthnicityWhite  
##                                                                 -0.054078  
##                                                         IncomeLevel>$150k  
##                                                                 -0.206336  
##                                                      IncomeLevel$100-150k  
##                                                                 -0.032560  
##                                                        IncomeLevel$20-50k  
##                                                                 -0.162937  
##                                                       IncomeLevel$50-100k  
##                                                                 -0.243375  
##                                                                       BMI  
##                                                                  0.039126  
##                                                           CESD.10baseline  
##                                                                  0.011728  
##                                                SmokingStatusFormer Smoker  
##                                                                 -0.290040  
##                                                 SmokingStatusNever Smoked  
##                                                                 -0.315386  
##                                            SmokingStatusOccasional Smoker  
##                                                                 -0.327601  
##                                                 RelationshipstatusMarried  
##                                                                 -0.176033  
##                                               RelationshipstatusSeparated  
##                                                                  0.061149  
##                                                  RelationshipstatusSingle  
##                                                                  0.068453  
##                                                 RelationshipstatusWidowed  
##                                                                  0.044016  
##                                               LivingstatusAssisted Living  
##                                                                  0.309546  
##                                                         LivingstatusHouse  
##                                                                 -0.239899  
##                                                         LivingstatusOther  
##                                                                 -0.451692  
##                                                                AnxietyYes  
##                                                                 -0.039052  
##                                                             MoodDisordYes  
##                                                                  0.112731  
##                                                         Chronicconditions  
##                                                                  0.042309  
##                    timefactor2:PandemicFU2 data collected before COVID-19  
##                                                                 -0.342007  
##                                            timefactor2:Age_sexFemales 65+  
##                                                                 -0.141938  
##                                            timefactor2:Age_sexMales 45-64  
##                                                                 -0.060952  
##                                              timefactor2:Age_sexMales 65+  
##                                                                 -0.124570  
##             PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  
##                                                                 -0.015392  
##             PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  
##                                                                  0.104367  
##               PandemicFU2 data collected before COVID-19:Age_sexMales 65+  
##                                                                  0.094448  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  
##                                                                 -0.173610  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  
##                                                                 -0.037797  
##   timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+  
##                                                                 -0.387304  
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 2 lme4 warnings

16.8.2.2) Predicted probabilities

ggpredict(sit4, c("Pandemic","timefactor","Age_sex"))

## # Predicted probabilities of SB.binary
## 
## # timefactor = 1
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.43 | [0.34, 0.52]
## FU2 data collected before COVID-19 |      0.42 | [0.34, 0.51]
## 
## # timefactor = 2
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.57 | [0.48, 0.66]
## FU2 data collected before COVID-19 |      0.53 | [0.44, 0.62]
## 
## # timefactor = 1
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.54 | [0.44, 0.63]
## FU2 data collected before COVID-19 |      0.52 | [0.43, 0.62]
## 
## # timefactor = 2
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.64 | [0.55, 0.72]
## FU2 data collected before COVID-19 |      0.55 | [0.46, 0.64]
## 
## # timefactor = 1
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.39 | [0.31, 0.48]
## FU2 data collected before COVID-19 |      0.43 | [0.34, 0.53]
## 
## # timefactor = 2
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.52 | [0.43, 0.61]
## FU2 data collected before COVID-19 |      0.48 | [0.39, 0.58]
## 
## # timefactor = 1
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.51 | [0.41, 0.61]
## FU2 data collected before COVID-19 |      0.53 | [0.43, 0.62]
## 
## # timefactor = 2
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.62 | [0.53, 0.71]
## FU2 data collected before COVID-19 |      0.51 | [0.41, 0.61]
## 
## Adjusted for:
## *  SBbaseline.binary =                        0
## *          Education = College Degree or Higher
## *          Ethnicity =                    Other
## *        IncomeLevel =                    <$20k
## *                BMI =                    27.50
## *    CESD.10baseline =                     4.88
## *      SmokingStatus =             Daily Smoker
## * Relationshipstatus =                 Divorced
## *       Livingstatus = Apartment/Condo/Townhome
## *            Anxiety =                       No
## *         MoodDisord =                       No
## *  Chronicconditions =                     2.71
## *                 ID =     0 (population-level)

16.8.2.3) Contrasts for predicted probilities

Mean differences and 95% CIs

sit.test4 <- as.data.frame(ggpredict(sit4, c("timefactor", "Pandemic", "Age_sex")))

#Females 45-64 years
mean.diff.Females.Young.5<-(subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
         
se.Females.Young.5<-(sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
     (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))

UL.Females.Young.5<-mean.diff.Females.Young.5 + se.Females.Young.5*1.96
LL.Females.Young.5<-mean.diff.Females.Young.5 - se.Females.Young.5*1.96

mean.diff.Females.Young.6<-(subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
         
se.Females.Young.6<-(sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
     (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))

UL.Females.Young.6<-mean.diff.Females.Young.6 + se.Females.Young.6*1.96
LL.Females.Young.6<-mean.diff.Females.Young.6 - se.Females.Young.6*1.96

#Females 65+ years
mean.diff.Females.Old.5<-(subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
         
se.Females.Old.5<-(sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
     (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))

UL.Females.Old.5<-mean.diff.Females.Old.5 + se.Females.Old.5*1.96
LL.Females.Old.5<-mean.diff.Females.Old.5 - se.Females.Old.5*1.96

mean.diff.Females.Old.6<-(subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
         
se.Females.Old.6<-(sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
     (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))

UL.Females.Old.6<-mean.diff.Females.Old.6 + se.Females.Old.6*1.96
LL.Females.Old.6<-mean.diff.Females.Old.6 - se.Females.Old.6*1.96

#Males 45-64 years
mean.diff.Males.Young.5<-(subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
         
se.Males.Young.5<-(sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
     (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))

UL.Males.Young.5<-mean.diff.Males.Young.5 + se.Males.Young.5*1.96
LL.Males.Young.5<-mean.diff.Males.Young.5 - se.Males.Young.5*1.96

mean.diff.Males.Young.6<-(subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
         
se.Males.Young.6<-(sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
     (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))

UL.Males.Young.6<-mean.diff.Males.Young.6 + se.Males.Young.6*1.96
LL.Males.Young.6<-mean.diff.Males.Young.6 - se.Males.Young.6*1.96

#Males 65+ years
mean.diff.Males.Old.5<-(subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
         
se.Males.Old.5<-(sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
     (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))

UL.Males.Old.5<-mean.diff.Males.Old.5 + se.Males.Old.5*1.96
LL.Males.Old.5<-mean.diff.Males.Old.5 - se.Males.Old.5*1.96

mean.diff.Males.Old.6<-(subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
         
se.Males.Old.6<-(sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
     (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))

UL.Males.Old.6<-mean.diff.Males.Old.6 + se.Males.Old.6*1.96
LL.Males.Old.6<-mean.diff.Males.Old.6 - se.Males.Old.6*1.96



#Mean differences and LL and UL
mean.diff.Females.Young.5

## [1] 0.004546467

UL.Females.Young.5

## [1] 0.3675635

LL.Females.Young.5

## [1] -0.3584706

mean.diff.Females.Young.6

## [1] 0.04191875

UL.Females.Young.6

## [1] 0.4037047

LL.Females.Young.6

## [1] -0.3198672

mean.diff.Females.Old.5

## [1] 0.01323892

UL.Females.Old.5

## [1] 0.3971556

LL.Females.Old.5

## [1] -0.3706777

mean.diff.Females.Old.6

## [1] 0.08643343

UL.Females.Old.6

## [1] 0.4707015

LL.Females.Old.6

## [1] -0.2978347

mean.diff.Males.Young.5

## [1] -0.04130713

UL.Males.Young.5

## [1] 0.330242

LL.Males.Young.5

## [1] -0.4128563

mean.diff.Males.Young.6

## [1] 0.03797661

UL.Males.Young.6

## [1] 0.4084541

LL.Males.Young.6

## [1] -0.3325009

mean.diff.Males.Old.5

## [1] -0.01664062

UL.Males.Old.5

## [1] 0.374

LL.Males.Old.5

## [1] -0.4072812

mean.diff.Males.Old.6

## [1] 0.1114892

UL.Males.Old.6

## [1] 0.5022129

LL.Males.Old.6

## [1] -0.2792345

Z-scores

z.Females.Young.5 <- (subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
     (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
     
z.Females.Young.6 <- (subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
         (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))

z.Females.Old.5 <- (subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
     (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
     
z.Females.Old.6 <- (subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
         (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))

z.Males.Young.5 <- (subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
     (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
     
z.Males.Young.6 <- (subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
         (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))

z.Males.Old.5 <- (subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
     (subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
     
z.Males.Old.6 <- (subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
         (subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))

#z-scores
z.Females.Young.5

## [1] 0.02454727

z.Females.Young.6

## [1] 0.2270977

z.Males.Young.5

## [1] -0.2179038

z.Males.Young.6

## [1] 0.2009141

z.Females.Old.5

## [1] 0.06758834

z.Females.Old.6

## [1] 0.4408628

z.Males.Old.5

## [1] -0.08349261

z.Males.Old.6

## [1] 0.5592669

p-values for z-scores

2*pnorm(z.Females.Young.5, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.9804161

2*pnorm(z.Females.Young.6, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.8203478

2*pnorm(z.Males.Young.5, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 1.172496

2*pnorm(z.Males.Young.6, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.8407657

2*pnorm(z.Females.Old.5, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.9461133

2*pnorm(z.Females.Old.6, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.6593123

2*pnorm(z.Males.Old.5, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 1.06654

2*pnorm(z.Males.Old.6, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.5759796

16.8.2.4) Graph of predicted probabilities

ggpredict(sit4, c("timefactor","Pandemic","Age_sex")) %>% plot()

16.9) Sleep Results

16.9.1) Full model adjusted for baseline

16.9.1.1) Model

sleep3 <- glmer(
  RSTLS_Sleep ~ timefactor*Pandemic + Age + Sex + RSTLS_Sleepbaseline + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = truncated.data_long_2, 
  family = binomial(link = "logit")
)

## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations

## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 3.52462 (tol = 0.002, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?

sleep3

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: 
## RSTLS_Sleep ~ timefactor * Pandemic + Age + Sex + RSTLS_Sleepbaseline +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: truncated.data_long_2
##       AIC       BIC    logLik  deviance  df.resid 
##  20204.61  20444.91 -10071.30  20142.61     17150 
## Random effects:
##  Groups Name        Std.Dev.
##  ID     (Intercept) 0.9005  
## Number of obs: 17181, groups:  ID, 8611
## Fixed Effects:
##                                            (Intercept)  
##                                             -1.0418064  
##                                            timefactor2  
##                                             -0.1945906  
##             PandemicFU2 data collected before COVID-19  
##                                             -0.0515297  
##                                                    Age  
##                                             -0.0119102  
##                                                   SexM  
##                                             -0.2589129  
##                                    RSTLS_Sleepbaseline  
##                                              0.9497744  
##                           EducationHigh School Diploma  
##                                              0.1890673  
##                 EducationLess than High School Diploma  
##                                              0.2068022  
##                                  EducationSome College  
##                                              0.1085545  
##                                         EthnicityWhite  
##                                              0.1331178  
##                                      IncomeLevel>$150k  
##                                             -0.0381059  
##                                   IncomeLevel$100-150k  
##                                              0.0865390  
##                                     IncomeLevel$20-50k  
##                                             -0.0187249  
##                                    IncomeLevel$50-100k  
##                                              0.0363318  
##                                                    BMI  
##                                             -0.0003967  
##                                        CESD.10baseline  
##                                              0.0632756  
##                             SmokingStatusFormer Smoker  
##                                              0.0443048  
##                              SmokingStatusNever Smoked  
##                                             -0.0658098  
##                         SmokingStatusOccasional Smoker  
##                                             -0.0235334  
##                              RelationshipstatusMarried  
##                                             -0.0057746  
##                            RelationshipstatusSeparated  
##                                             -0.4337311  
##                               RelationshipstatusSingle  
##                                             -0.1615102  
##                              RelationshipstatusWidowed  
##                                             -0.1852212  
##                            LivingstatusAssisted Living  
##                                              0.1545573  
##                                      LivingstatusHouse  
##                                              0.1093385  
##                                      LivingstatusOther  
##                                              0.0254967  
##                                             AnxietyYes  
##                                             -0.1289796  
##                                          MoodDisordYes  
##                                             -0.0165015  
##                                      Chronicconditions  
##                                              0.0909348  
## timefactor2:PandemicFU2 data collected before COVID-19  
##                                              0.0442628  
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 3 lme4 warnings

16.9.1.2) Predicted probabilities

ggpredict(sleep3, c("timefactor", "Pandemic"))

## # Predicted probabilities of RSTLS_Sleep
## 
## # Pandemic = FU2 data collected after COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.29 | [0.22, 0.37]
## 2          |      0.25 | [0.19, 0.32]
## 
## # Pandemic = FU2 data collected before COVID-19
## 
## timefactor | Predicted |       95% CI
## -------------------------------------
## 1          |      0.28 | [0.21, 0.36]
## 2          |      0.25 | [0.19, 0.32]
## 
## Adjusted for:
## *                 Age =                    60.00
## *                 Sex =                        F
## * RSTLS_Sleepbaseline =                     0.33
## *           Education = College Degree or Higher
## *           Ethnicity =                    Other
## *         IncomeLevel =                    <$20k
## *                 BMI =                    27.51
## *     CESD.10baseline =                     4.90
## *       SmokingStatus =             Daily Smoker
## *  Relationshipstatus =                 Divorced
## *        Livingstatus = Apartment/Condo/Townhome
## *             Anxiety =                       No
## *          MoodDisord =                       No
## *   Chronicconditions =                     2.71
## *                  ID =     0 (population-level)

16.9.1.3) Contrasts for predicted probilities

Mean Differences and 95% CIs

sleep3.test <- as.data.frame(ggpredict(sleep3, c("timefactor", "Pandemic")))

mean.diff.7<-(subset(sleep3.test,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep3.test,x==1 & group == "FU2 data collected before COVID-19")$predicted)
         
se.7<-(sqrt(((subset(sleep3.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
         (subset(sleep3.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))

UL.7<-mean.diff.7 + se.7*1.96
LL.7<-mean.diff.7 - se.7*1.96

         
mean.diff.8<-(subset(sleep3.test,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep3.test,x==2 & group == "FU2 data collected before COVID-19")$predicted)
         
se.8<-(sqrt(((subset(sleep3.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
         (subset(sleep3.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))

UL.8<-mean.diff.8 + se.6*1.96
LL.8<-mean.diff.8 - se.6*1.96

#Mean differences and LL and UL
mean.diff.7

## [1] 0.01047513

UL.7

## [1] 0.3659816

LL.7

## [1] -0.3450313

mean.diff.8

## [1] 0.001363091

UL.8

## [1] 0.3475642

LL.8

## [1] -0.344838

sleep3.test <- as.data.frame(ggpredict(sleep3, c("timefactor", "Pandemic")))

z.7<- (subset(sleep3.test,x==1 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep3.test,x==1 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sleep3.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
                                                                                                     (subset(sleep3.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))

z.8<- (subset(sleep3.test,x==2 & group == "FU2 data collected after COVID-19")$predicted - 
         subset(sleep3.test,x==2 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sleep3.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
                                                                                                     (subset(sleep3.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))

#z-scores
z.7

## [1] 0.05775215

z.8

## [1] 0.007501955

p-values for z-scores

2*pnorm(z.7, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.9539461

2*pnorm(z.8, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.9940144

16.9.1.4) Graph of predicted probabilities

ggpredict(sleep3, c("timefactor", "Pandemic")) %>% plot()

16.9.2) Full model adjusted for baseline (age and sex differences)

16.9.2.1) Model

sleep4 <- glmer(
  RSTLS_Sleep ~ timefactor*Pandemic*Age_sex + RSTLS_Sleepbaseline + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + 
    Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + 
    (1|ID), 
  data = truncated.data_long_2, 
  family = binomial(link = "logit")
)

## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations

## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 4.71501 (tol = 0.002, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

sleep4

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: RSTLS_Sleep ~ timefactor * Pandemic * Age_sex + RSTLS_Sleepbaseline +  
##     Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +  
##     SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +  
##     MoodDisord + Chronicconditions + (1 | ID)
##    Data: truncated.data_long_2
##       AIC       BIC    logLik  deviance  df.resid 
##  20201.13  20518.95 -10059.57  20119.13     17140 
## Random effects:
##  Groups Name        Std.Dev.
##  ID     (Intercept) 0.9069  
## Number of obs: 17181, groups:  ID, 8611
## Fixed Effects:
##                                                               (Intercept)  
##                                                                -1.6343390  
##                                                               timefactor2  
##                                                                -0.2754714  
##                                PandemicFU2 data collected before COVID-19  
##                                                                -0.1104027  
##                                                        Age_sexFemales 65+  
##                                                                -0.2002653  
##                                                        Age_sexMales 45-64  
##                                                                -0.5172539  
##                                                          Age_sexMales 65+  
##                                                                -0.4712077  
##                                                       RSTLS_Sleepbaseline  
##                                                                 0.9546104  
##                                              EducationHigh School Diploma  
##                                                                 0.1797473  
##                                    EducationLess than High School Diploma  
##                                                                 0.2136112  
##                                                     EducationSome College  
##                                                                 0.1332032  
##                                                            EthnicityWhite  
##                                                                 0.1308369  
##                                                         IncomeLevel>$150k  
##                                                                -0.0100607  
##                                                      IncomeLevel$100-150k  
##                                                                 0.1190651  
##                                                        IncomeLevel$20-50k  
##                                                                -0.0225508  
##                                                       IncomeLevel$50-100k  
##                                                                 0.0418234  
##                                                                       BMI  
##                                                                 0.0009517  
##                                                           CESD.10baseline  
##                                                                 0.0635611  
##                                                SmokingStatusFormer Smoker  
##                                                                 0.0340065  
##                                                 SmokingStatusNever Smoked  
##                                                                -0.0633799  
##                                            SmokingStatusOccasional Smoker  
##                                                                -0.0628344  
##                                                 RelationshipstatusMarried  
##                                                                -0.0255264  
##                                               RelationshipstatusSeparated  
##                                                                -0.4013104  
##                                                  RelationshipstatusSingle  
##                                                                -0.1510180  
##                                                 RelationshipstatusWidowed  
##                                                                -0.2338176  
##                                               LivingstatusAssisted Living  
##                                                                 0.1620799  
##                                                         LivingstatusHouse  
##                                                                 0.1285654  
##                                                         LivingstatusOther  
##                                                                -0.0917031  
##                                                                AnxietyYes  
##                                                                -0.1349351  
##                                                             MoodDisordYes  
##                                                                -0.0118410  
##                                                         Chronicconditions  
##                                                                 0.0886673  
##                    timefactor2:PandemicFU2 data collected before COVID-19  
##                                                                 0.1061261  
##                                            timefactor2:Age_sexFemales 65+  
##                                                                 0.0848093  
##                                            timefactor2:Age_sexMales 45-64  
##                                                                 0.2520917  
##                                              timefactor2:Age_sexMales 65+  
##                                                                -0.1858191  
##             PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  
##                                                                -0.2228146  
##             PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  
##                                                                 0.3026941  
##               PandemicFU2 data collected before COVID-19:Age_sexMales 65+  
##                                                                 0.0524889  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+  
##                                                                -0.1033567  
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64  
##                                                                -0.2996806  
##   timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+  
##                                                                 0.3004752  
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 2 lme4 warnings

16.9.2.2) Predicted probabilities

ggpredict(sleep4, c("Pandemic","timefactor","Age_sex"))

## # Predicted probabilities of RSTLS_Sleep
## 
## # timefactor = 1
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.32 | [0.25, 0.41]
## FU2 data collected before COVID-19 |      0.30 | [0.23, 0.38]
## 
## # timefactor = 2
## #    Age_sex = Females 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.27 | [0.20, 0.34]
## FU2 data collected before COVID-19 |      0.26 | [0.20, 0.34]
## 
## # timefactor = 1
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.28 | [0.21, 0.37]
## FU2 data collected before COVID-19 |      0.22 | [0.16, 0.29]
## 
## # timefactor = 2
## #    Age_sex = Females 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.24 | [0.18, 0.32]
## FU2 data collected before COVID-19 |      0.19 | [0.13, 0.26]
## 
## # timefactor = 1
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.22 | [0.16, 0.29]
## FU2 data collected before COVID-19 |      0.26 | [0.19, 0.34]
## 
## # timefactor = 2
## #    Age_sex = Males 45-64
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.22 | [0.16, 0.29]
## FU2 data collected before COVID-19 |      0.22 | [0.16, 0.29]
## 
## # timefactor = 1
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.23 | [0.17, 0.31]
## FU2 data collected before COVID-19 |      0.22 | [0.16, 0.30]
## 
## # timefactor = 2
## #    Age_sex = Males 65+
## 
## Pandemic                           | Predicted |       95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19  |      0.16 | [0.11, 0.22]
## FU2 data collected before COVID-19 |      0.21 | [0.15, 0.28]
## 
## Adjusted for:
## * RSTLS_Sleepbaseline =                     0.33
## *           Education = College Degree or Higher
## *           Ethnicity =                    Other
## *         IncomeLevel =                    <$20k
## *                 BMI =                    27.51
## *     CESD.10baseline =                     4.90
## *       SmokingStatus =             Daily Smoker
## *  Relationshipstatus =                 Divorced
## *        Livingstatus = Apartment/Condo/Townhome
## *             Anxiety =                       No
## *          MoodDisord =                       No
## *   Chronicconditions =                     2.71
## *                  ID =     0 (population-level)

16.9.2.3) Contrasts for predicted probilities

Mean differences and 95% CIs

sleep.test4 <- as.data.frame(ggpredict(sleep4, c("timefactor", "Pandemic", "Age_sex")))

#Females 45-64 years
mean.diff.Females.Young.7<-(subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
         
se.Females.Young.7<-(sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
     (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))

UL.Females.Young.7<-mean.diff.Females.Young.7 + se.Females.Young.7*1.96
LL.Females.Young.7<-mean.diff.Females.Young.7 - se.Females.Young.7*1.96

mean.diff.Females.Young.8<-(subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
         subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
         
se.Females.Young.8<-(sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
     (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))

UL.Females.Young.8<-mean.diff.Females.Young.8 + se.Females.Young.8*1.96
LL.Females.Young.8<-mean.diff.Females.Young.8 - se.Females.Young.8*1.96

#Females 65+ years
mean.diff.Females.Old.7<-(subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
         
se.Females.Old.7<-(sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
     (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))

UL.Females.Old.7<-mean.diff.Females.Old.7 + se.Females.Old.7*1.96
LL.Females.Old.7<-mean.diff.Females.Old.7 - se.Females.Old.7*1.96

mean.diff.Females.Old.8<-(subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
         subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
         
se.Females.Old.8<-(sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
     (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))

UL.Females.Old.8<-mean.diff.Females.Old.8 + se.Females.Old.8*1.96
LL.Females.Old.8<-mean.diff.Females.Old.8 - se.Females.Old.8*1.96

#Males 45-64 years
mean.diff.Males.Young.7<-(subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
         
se.Males.Young.7<-(sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
     (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))

UL.Males.Young.7<-mean.diff.Males.Young.7 + se.Males.Young.7*1.96
LL.Males.Young.7<-mean.diff.Males.Young.7 - se.Males.Young.7*1.96

mean.diff.Males.Young.8<-(subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
         subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
         
se.Males.Young.8<-(sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
     (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))

UL.Males.Young.8<-mean.diff.Males.Young.8 + se.Males.Young.8*1.96
LL.Males.Young.8<-mean.diff.Males.Young.8 - se.Males.Young.8*1.96

#Males 65+ years
mean.diff.Males.Old.7<-(subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
         
se.Males.Old.7<-(sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
     (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))

UL.Males.Old.7<-mean.diff.Males.Old.7 + se.Males.Old.7*1.96
LL.Males.Old.7<-mean.diff.Males.Old.7 - se.Males.Old.7*1.96

mean.diff.Males.Old.8<-(subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
         subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
         
se.Males.Old.8<-(sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
     (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))

UL.Males.Old.8<-mean.diff.Males.Old.8 + se.Males.Old.8*1.96
LL.Males.Old.8<-mean.diff.Males.Old.8 - se.Males.Old.8*1.96



#Mean differences and LL and UL
mean.diff.Females.Young.7

## [1] 0.02362387

UL.Females.Young.7

## [1] 0.3940529

LL.Females.Young.7

## [1] -0.3468051

mean.diff.Females.Young.8

## [1] 0.0008325664

UL.Females.Young.8

## [1] 0.3723871

LL.Females.Young.8

## [1] -0.370722

mean.diff.Females.Old.7

## [1] 0.06207411

UL.Females.Old.7

## [1] 0.4573061

LL.Females.Old.7

## [1] -0.3331579

mean.diff.Females.Old.8

## [1] 0.05562014

UL.Females.Old.8

## [1] 0.452915

LL.Females.Old.8

## [1] -0.3416747

mean.diff.Males.Young.7

## [1] -0.03485029

UL.Males.Young.7

## [1] 0.3462642

LL.Males.Young.7

## [1] -0.4159648

mean.diff.Males.Young.8

## [1] 0.0002144097

UL.Males.Young.8

## [1] 0.3825704

LL.Males.Young.8

## [1] -0.3821416

mean.diff.Males.Old.7

## [1] 0.01005995

UL.Males.Old.7

## [1] 0.4134149

LL.Males.Old.7

## [1] -0.393295

mean.diff.Males.Old.8

## [1] -0.05197637

UL.Males.Old.8

## [1] 0.3554982

LL.Males.Old.8

## [1] -0.4594509

Z-scores

z.Females.Young.7 <- (subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
                        subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
                                                                                                                                                (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))

z.Females.Young.8 <- (subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted - 
                        subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
                                                                                                                                                (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))

z.Females.Old.7 <- (subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
                      subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
                                                                                                                                            (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))

z.Females.Old.8 <- (subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted - 
                      subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
                                                                                                                                            (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))

z.Males.Young.7 <- (subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
                      subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
                                                                                                                                            (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))

z.Males.Young.8 <- (subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted - 
                      subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
                                                                                                                                            (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))

z.Males.Old.7 <- (subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
                    subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
                                                                                                                                        (subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))

z.Males.Old.8 <- (subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted - 
                    subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
                                                                                                                                        (subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))

#z-scores
z.Females.Young.7

## [1] 0.1249977

z.Females.Young.8

## [1] 0.004391899

z.Males.Young.7

## [1] -0.1792285

z.Males.Young.8

## [1] 0.001099088

z.Females.Old.7

## [1] 0.3078325

z.Females.Old.8

## [1] 0.2743944

z.Males.Old.7

## [1] 0.04888373

z.Males.Old.8

## [1] -0.2500124

p-values for z-scores

2*pnorm(z.Females.Young.7, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.9005253

2*pnorm(z.Females.Young.8, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.9964958

2*pnorm(z.Males.Young.7, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 1.142242

2*pnorm(z.Males.Young.8, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.9991231

2*pnorm(z.Females.Old.7, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.7582098

2*pnorm(z.Females.Old.8, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.7837816

2*pnorm(z.Males.Old.7, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 0.961012

2*pnorm(z.Males.Old.8, mean = 0, sd = 1, lower.tail = FALSE)

## [1] 1.197422

16.9.2.4) Graph of predicted probabilities

ggpredict(sleep4, c("timefactor","Pandemic","Age_sex")) %>% plot()